types of nanoparticles research paper

• Open access
• Published: 07 June 2022

Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists

• Nadeem Joudeh 1 &
• Dirk Linke 1  

Journal of Nanobiotechnology volume  20 , Article number:  262 ( 2022 ) Cite this article

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Interest in nanomaterials and especially nanoparticles has exploded in the past decades primarily due to their novel or enhanced physical and chemical properties compared to bulk material. These extraordinary properties have created a multitude of innovative applications in the fields of medicine and pharma, electronics, agriculture, chemical catalysis, food industry, and many others. More recently, nanoparticles are also being synthesized ‘biologically’ through the use of plant- or microorganism-mediated processes, as an environmentally friendly alternative to the expensive, energy-intensive, and potentially toxic physical and chemical synthesis methods. This transdisciplinary approach to nanoparticle synthesis requires that biologists and biotechnologists understand and learn to use the complex methodology needed to properly characterize these processes. This review targets a bio-oriented audience and summarizes the physico–chemical properties of nanoparticles, and methods used for their characterization. It highlights why nanomaterials are different compared to micro- or bulk materials. We try to provide a comprehensive overview of the different classes of nanoparticles and their novel or enhanced physicochemical properties including mechanical, thermal, magnetic, electronic, optical, and catalytic properties. A comprehensive list of the common methods and techniques used for the characterization and analysis of these properties is presented together with a large list of examples for biogenic nanoparticles that have been previously synthesized and characterized, including their application in the fields of medicine, electronics, agriculture, and food production. We hope that this makes the many different methods more accessible to the readers, and to help with identifying the proper methodology for any given nanoscience problem.

Nano etymology

The prefix nano is derived from the Greek word nanos, “a dwarf”. In 1947, at the 14th conference of the International Union of Pure and Applied Chemistry (IUPAC), the prefix nano was officially adopted to describe the one-billionth part (10 –9 ) of a unit Footnote 1 . In scientific literature, the prefix nano has been adopted as a popular label in many fields of modern science to describe small entities and processes. These terms include, but are not limited to nanoscience, nanotechnology, nanorobots, nanomagnets, nanoelectronics, nanoencapsulation, etc. [ 1 ]. In all of these cases, the prefix nano is used to describe “very small” entities or processes, most often at actual nanometer scale.

Definitions

Nanoscience is a branch of science that comprises the study of properties of matter at the nanoscale, and particularly focuses on the unique, size-dependent properties of solid-state materials [ 2 ]. Nanotechnology is the branch that comprises the synthesis, engineering, and utilization of materials whose size ranges from 1 to 100 nm, known as nanomaterials [ 3 ]. The birth of nanoscience and nanotechnology concepts is usually linked to the famous lecture of Nobel laureate Richard Feynman at the 1959 meeting of the American Physical Society, ‘‘There’s Plenty of Room at the Bottom’’ [ 4 ]. However, the use of nanotechnology and nanomaterials goes back in history long before that.

History of nanotechnology

Long before the era of nanotechnology, people were unknowingly coming across various nanosized objects and using nano-level processes. In ancient Egypt, dyeing hair in black was common and was for a long time believed to be based on plant products such as henna [ 5 ]. However, recent research on hair samples from ancient Egyptian burial sites showed that hair was dyed with paste from lime, lead oxide, and water [ 6 ]. In this dyeing process, galenite (lead sulfide, PbS) nanoparticles are formed. The ancient Egyptians were able to make the dyeing paste react with sulfur (part of hair keratin) and produce small PbS nanoparticles which provided even and steady dyeing.

Probably the most famous example for the ancient use of nanotechnology is the Lycurgus Cup (fourth century CE). This ancient roman cup possesses unusual optical properties; it changes its color based on the location of the light source. In natural light, the cup is green, but when it is illuminated from within (with a candle), it becomes red. The recent analysis of this cup showed that it contains 50–100 nm Au and Ag nanoparticles [ 7 ], which are responsible for the unusual coloring of the cup through the effects of plasmon excitation of electrons [ 8 ]. The ancient use of nanotechnology does not stop here, in fact, there is evidence for the early use of nanotechnology processes in Mesopotamia, Ancient India, and the Maya [ 9 , 10 ].

Why nanomaterials are different

Today, due to their unique properties, nanomaterials are used in a wide range of applications, such as catalysis, water treatment, energy storage, medicine, agriculture, etc . [ 11 , 12 , 13 ]. Two main factors cause nanomaterials to behave significantly differently than the same materials at larger dimensions: surface effects and quantum effects [ 14 ]. These factors make nanomaterials exhibit enhanced or novel mechanical, thermal, magnetic, electronic, optical, and catalytic properties [ 1 , 15 , 16 ].

Nanomaterials have different surface effects compared to micromaterials or bulk materials, mainly due to three reasons; (a) dispersed nanomaterials have a very large surface area and high particle number per mass unit, (b) the fraction of atoms at the surface in nanomaterials is increased, and (c) the atoms situated at the surface in nanomaterials have fewer direct neighbors [ 1 , 14 ]. As a consequence of each of these differences, the chemical and physical properties of nanomaterials change compared to their larger-dimension counterparts. For instance, having fewer direct neighbor atoms for the atoms situated at the surface results in lowering the binding energy per atom for nanomaterials. This change directly affects the melting temperature of nanomaterials following the Gibbs–Thomson equation, e.g., the melting point of 2.5 nm gold nanoparticles is 407 degrees lower than the melting point of bulk gold [ 14 ]. Larger surface areas and larger surface-to-volume ratios generally increases the reactivity of nanomaterials due to the larger reaction surface [ 1 ], as well as resulting in significant effects of surface properties on their structure [ 17 ]. The dispersity of nanomaterials is a key factor for the surface effects. The strong attractive interactions between particles can result in the agglomeration and aggregation of nanomaterials, which negatively affects their surface area and their nanoscale properties [ 18 ]. Agglomeration can be prevented by increasing the zeta potential of nanomaterials (increasing the repulsive force) [ 19 ], optimizing the degree of hydrophilicity/hydrophobicity of the nanomaterial, or by optimizing the pH and the ionic strength of the suspension medium [ 20 ].

Nanomaterials display distinct size-dependent properties in the 1–100 nm range where quantum phenomena are involved. When the material radius approaches the asymptotic exciton Bohr radius (the separation distance between the electron and hole), the influence of quantum confinement becomes apparent [ 17 ]. In other words, by shrinking the size of the material, quantum effects become more pronounced, and nanomaterials become quantal. Those quantum structures are physical structures where all the charge carriers (electrons and holes) are confined within the physical dimensions [ 21 ]. As a result of quantum confinement effects, for instance, some non-magnetic materials in bulk such as palladium, platinum, and gold become magnetic in the nanoscale [ 14 ]. Quantum confinement can also result in significant changes in electron affinity or the ability to accept or donate electrical charges, which is directly reflected on the catalytic properties of the material. For example, the catalytic activity of cationic platinum clusters in N 2 O decomposition is dictated by the number of atoms in the cluster. 6–9, 11, 12, 15, and 20 atom-containing clusters are very reactive, while clusters with 10, 13, 14, and 19 atoms have low reactivity [ 14 ].

Classification of nanomaterials

The key elements of nanotechnology are the nanomaterials. Nanomaterials are defined as materials where at least one of their dimensions is in the nanoscale, i.e. smaller than 100 nm [ 22 ]. Based on their dimensionalities, nanomaterials are placed into four different classes, summarized in Fig.  1 .

Zero-dimensional nanomaterials (0-D): the nanomaterials in this class have all their three dimensions in the nanoscale range. Examples are quantum dots, fullerenes, and nanoparticles.

One-dimensional nanomaterials (1-D): the nanomaterials in this class have one dimension outside the nanoscale. Examples are nanotubes, nanofibers, nanorods, nanowires, and nanohorns.

Two-dimensional nanomaterials (2-D): the nanomaterials in this class have two dimensions outside the nanoscale. Examples are nanosheets, nanofilms, and nanolayers.

Three-dimensional nanomaterials (3-D) or bulk nanomaterials: in this class the materials are not confined to the nanoscale in any dimension. This class contains bulk powders, dispersions of nanoparticles, arrays of nanowires and nanotubes, etc .

Nanomaterials classification based on dimensionality

Nanoparticles (NPs)

The International Organization for Standardization (ISO) defines nanoparticles as nano-objects with all external dimensions in the nanoscale, where the lengths of the longest and the shortest axes of the nano-object do not differ significantly. If the dimensions differ significantly (typically by more than three times), terms such as nanofibers or nanoplates maybe preferred to the term NPs Footnote 2 .

NPs can be of different shapes, sizes, and structures. They can be spherical, cylindrical, conical, tubular, hollow core, spiral, etc., or irregular [ 23 ]. The size of NPs can be anywhere from 1 to 100 nm. If the size of NPs gets lower than 1 nm, the term atom clusters is usually preferred. NPs can be crystalline with single or multi-crystal solids, or amorphous. NPs can be either loose or agglomerated [ 24 ].

NPs can be uniform, or can be composed of several layers. In the latter case, the layers often are: (a) The surface layer, which usually consists of a variety of small molecules, metal ions, surfactants, or polymers. (b) The shell layer, which is made of a chemically different material from the core layer. (c) The core layer, which is the central portion of the NP [ 25 ].

Classification of NPs

Based on their composition, NPs are generally placed into three classes: organic, carbon-based, and inorganic [ 23 ].

Organic NPs

This class comprises NPs that are made of proteins, carbohydrates, lipids, polymers, or any other organic compounds [ 26 ]. The most prominent examples of this class are dendrimers, liposomes, micelles, and protein complexes such as ferritin (shown in Fig.  2 ). These NPs are typically non-toxic, bio-degradable, and can in some cases, e.g., for liposomes, have a hollow core. Organic NPs are sensitive to thermal and electromagnetic radiation such as heat and light [ 23 ]. In addition, they are often formed by non-covalent intermolecular interactions, which makes them more labile in nature and offers a route for clearance from the body [ 27 ]. There are different parameters that determine the potential field of application of organic NPs, e.g., composition, surface morphology, stability, carrying capacity, etc . Today, organic NPs are mostly used in the biomedical field in targeted drug delivery [ 23 ] and cancer therapy [ 28 ].

Types of organic NPs. A Dendrimers; B liposomes; C micelles; and D ferritin

Carbon-based NPs

This class comprises NPs that are made solely from carbon atoms [ 23 ]. Famous examples of this class are fullerenes, carbon black NPs, and carbon quantum dots (shown in Fig.  3 ). Fullerenes are carbon molecules that are characterized by a symmetrical closed-cage structure. C 60 fullerenes consist of 60 carbon atoms arranged in the shape of a soccer ball [ 29 ], but also other types of fullerenes such as C 70 and C 540 fullerenes have been described [ 30 ]. Carbon black NPs are grape-like aggregates of highly fused spherical particles [ 31 ]. Carbon quantum dots consist of discrete, quasi-spherical carbon NPs with sizes below 10 nm [ 32 ]. Carbon-based NPs unite the distinctive properties of sp 2 -hybridized carbon bonds with the unusual physicochemical properties at the nanoscale. Due to their unique electrical conductivity, high strength, electron affinity, optical, thermal, and sorption properties [ 25 , 33 ], carbon-based NPs are used in a wide range of application such as drug delivery [ 34 ], energy storage [ 35 ], bioimaging [ 36 ], photovoltaic devices, and environmental sensing applications to monitor microbial ecology or to detect microbial pathogens [ 33 ]. Nanodiamonds and carbon nano onions are more complex, carbon-based NPs. Due to their characteristic low toxicity and biocompatibility, they are used in drug delivery and tissue engineering applications [ 37 , 38 ].

Different types of carbon-based NPs. A C 60 fullerene; B carbon black NPs; and C carbon quantum dots

Inorganic NPs

This class comprises NPs that not made of carbon or organic materials. The typical examples of this class are metal, ceramic, and semiconductor NPs. Metal NPs are purely made of metal precursors, they can be monometallic, bimetallic [ 39 ], or polymetallic [ 40 ]. Bimetallic NPs can be made from alloys or formed in different layers (core–shell) [ 39 ]. Due to the localized surface plasmon resonance characteristics, these NPs possess unique optical and electricals properties [ 25 ]. In addition, some metal NPs also possess unique thermal, magnetic, and biological properties [ 23 ]. This makes them increasingly important materials for the development of nanodevices that can be used in numerous physical, chemical, biological, biomedical, and pharmaceutical applications [ 41 , 42 ] (these applications are discussed in detail later in the applications section of the review). In present days, the size-, shape-, and facet-controlled synthesis of metal NPs is important for creating cutting-edge materials [ 43 ].

Semiconductor NPs are made of semiconductor materials, which possess properties between metals and non-metals. These NPs possess unique wide bandgaps and show significant alteration in their properties with bandgap tuning compared to bulk semiconductor materials [ 25 ]. As a result, these NPs are important materials in photocatalysis, optic, and electronic devices [ 44 , 45 ]. Ceramic NPs are inorganic solids made of carbonates, carbides, phosphates, and oxides of metals and metalloids, such as titanium and calcium [ 46 ]. They are usually synthesized via heat and successive cooling and they can be found in amorphous, polycrystalline, dense, porous or hollow forms [ 25 ]. They are mainly used in biomedical applications due to their high stability and high load capacity [ 47 ]. Nevertheless, they are also used in other applications such as catalysis, degradation of dyes, photonics and optoelectronics [ 46 , 48 ].

Physicochemical properties of NPs

As mentioned earlier, NPs can be used in a long list of applications due to their unique physical and chemical properties that do not exist in their larger-dimension counterparts of the same materials. The following sections summarize the most import physicochemical properties that are changing on the nanoscale.

Mechanical properties

Mechanical properties refer to the mechanical characteristics of a material under different conditions, environments, and various external forces. As for traditional materials, the mechanical properties of nanomaterials generally consist of ten parts: strength, brittleness, hardness, toughness, fatigue strength, plasticity, elasticity, ductility, rigidity, and yield stress [ 49 ]. Most inorganic, non-metallic materials are brittle materials and do not have significant toughness, plasticity, elasticity, or ductility properties. Organic materials on the other hand, are flexible materials and do not necessarily have brittleness and rigidity properties.

Due to surface and quantum effects, NPs display different mechanical properties compared to bulk materials [ 49 ]. For example, conventional FeAl powder which is composed of microparticles (larger than 4 µm), is brittle, while ultrafine FeAl alloy powder displays a good combination of strength and ductility as well as enhanced plasticity [ 50 ]. These new properties are believed to arise due to the diverse interaction forces between NPs or between them and a surface. The most important interaction forces involved are van der Waals forces, which consist of three parts, Keesom force, Debye force, and London force [ 51 , 52 , 53 ]. Other relevant interaction forces are electrostatic and electrical double layer forces, normal and lateral capillary forces, solvation, structural, and hydration forces [ 54 ].

There are different theories on how the interaction forces between NPs give them new mechanical properties, such as the DLVO (Derjaguin–Landau–Verwey–Overbeek) theory, JKR (Johnson–Kendall–Roberts) theory, and DMT (Derjaguin–Muller–Toporov) theory. The DLVO theory combines the effects of van der Waals attraction and electrostatic repulsion to describe the stability of colloidal dispersions [ 54 ]. This theory can explain many phenomena in colloidal science, such as the adsorption and the aggregation of NPs in aqueous solutions and the force between charged surfaces interacting through a liquid medium [ 55 , 56 ]. Nevertheless, the DLVO theory is inadequate for the colloidal properties in the aggregated state [ 54 ].

When the size of objects decreases to the nanoscale, the surface forces become a major player in their adhesion, contact, and deformation behaviors. The JRK theory is applicable to easily deformable, large bodies with high surface energies, where it describes the domination of surface interactions by strong, short-range adhesion forces. In contrast to this, the DMT theory is applicable to very small and hard bodies with low surface energies, where it describes the adhesion being caused by the presence of weak, long-range attractive forces. Although the DLVO, JKR and DMT theories have been widely used to describe and study the mechanical properties of NPs [ 57 , 58 ], it is still a matter of debate whether or not continuum mechanics can be used to describe a particle or collection of particles at the nanometer scale [ 54 ].

Thermal properties

Heat transfer in NPs primarily depends on energy conduction due to electrons as well as photons (lattice vibration) and the scattering effects that accompany both [ 59 ]. The major components of thermal properties of a material are thermal conductivity, thermoelectric power, heat capacity, and thermal stability [ 59 , 60 ].

NP size has a direct impact on electrical and thermal conductivity of NPs [ 60 ]. As the NP size decreases, the ratio of particle surface area respective to its volume increases hyperbolically [ 60 ]. Since the conduction of electrons is one of the two main ways in which heat is transferred, the higher surface-to-volume ratio in NPs provides higher number of electrons for heat transfer compared to bulk materials [ 61 ]. Moreover, thermal conductivity in NPs is also promoted by microconvection, which results from the Brownian motion of NPs [ 62 ]. Nevertheless, this phenomenon only happens when solid NPs are dispersed in a liquid (generating a Nanofluid) [ 63 ]. As an example, the addition of Cu NPs to ethylene glycol enhances the thermal conductivity of the fluid up to 40% [ 64 ].

The thermoelectric power of a material depends on its Seebeck coefficient and electrical conductivity ( \(P={S}^{2}\sigma \) , where P is thermoelectric power, S is the Seebeck coefficient, and \(\sigma \) is the electrical conductivity). The scattering of NPs in bulk materials (doping) is known to enhance the thermoelectric power factor [ 65 ]. This enhancement could come from the enhancement of the Seebeck coefficient or the enhancement of electrical conductivity. The embedding of size-controlled NPs in bulk thermoelectric materials helps to reduce the lattice thermal conductivity and enhances the Seebeck coefficient due to electron energy filtering [ 66 , 67 ]. Generally, the enhancement of electrical conductivity is accompanied by the reduction of the Seebeck coefficient and vice versa [ 65 ] However, the doping of InGaAlAs material with 2–3 nm Er NPs resulted in the significant increase of thermoelectric power of the material through the enhancement of the conductivity while keeping the Seebeck coefficient unchanged [ 65 ]. Depending on NP size, volume fraction, and band offset, a NP-doped sample can either enhance or suppress the electrical conductivity in comparison with undoped bulk sample.

Experimental studies have shown that the heat capacity of NPs exceeds the values of analogous bulk materials by up to 10% [ 68 ], e.g. in the case of Al 2 O 3 and SiO 2 NPs [ 69 , 70 ]. The major contribution to heat capacity at ambient temperatures is determined by the vibration degrees of freedom, i.e., the peculiarities of phonon spectra (vibrational energy that arises from oscillating atoms within a crystal) are responsible for the anomalous behavior of heat capacity of NPs [ 68 ]. NPs usually exhibit a significant decrease in melting temperature compared to their analogous bulk materials [ 71 ]. The main reason for this phenomenon is that the liquid/vapor interface energy is generally lower than the average solid/vapor interface energy [ 72 ]. When the particle size decreases, its surface-to-volume ratio increases, and the melting temperature decreases as a result of the improved free energy at the particle surface [ 73 ]. For instance, the melting temperature of 3 nm Au NPs is 300 degrees lower than the melting temperature of bulk gold [ 14 ]. In addition, NP composition plays an important role in thermal stability. For example, the thermal stability of Au in Au 0.8 Fe 0.2 is significantly higher than of pure Au or Au 0.2 Fe 0.8 [ 74 ]. Generally, bimetallic alloy NPs show higher thermal stabilities and melting temperatures than monometallic NPs due to the alloying effect [ 75 , 76 ].

Magnetic properties

All magnetic compounds include a ‘magnetic element’ in their formula, i.e., Fe, Co, or Ni (at ambient temperatures). There are only three known exceptions that are made from mixed diamagnetic elements, Sc 3 In, ZrZn 2 , and TiBe 2-x Cu x [ 77 , 78 , 79 , 80 ]. Otherwise, elements such as Pd, Au, or Ag are diamagnetic. This all changes in the nanoscale. Several materials become magnetic in the form of NPs as a result of uneven electronic distribution [ 25 ]. For instance, FeAl is not magnetic in bulk but in the form of NPs, it is becomes magnetic [ 50 ], other examples include Pd and Au [ 81 ]. In bulk materials, the key parameters for determining magnetic properties are composition, crystallographic structure, magnetic anisotropy, and vacancy defects [ 82 , 83 ]. However, on the nanoscale, two more important parameters are strongly involved, i.e., size and shape [ 84 ].

One of the interesting size-dependent phenomena of NPs is superparamagnetism [ 84 ]. As the size of the NPs decreases, the magnetic anisotropy energy per NP decreases. The magnetic anisotropy energy is the energy keeping the magnetic moment in a particular orientation. At a characteristic size for each type of NPs, the anisotropy energy becomes equal to the thermal energy, which allows the random flipping of the magnetic moment [ 85 ], in this case, the NP is defined as being superparamagnetic [ 86 ]. Superparamagnetic NPs display high magnetization only in the presence of a magnetic field, and once it is removed they do not retain any magnetization [ 87 ]. Superparamagnetism was long believed to form only in small ferromagnetic or ferrimagnetic NPs [ 88 ], but interestingly, other paramagnetic materials show magnetism in the nanoscale too [ 81 ].

NP size effects can also be observed in changes in magnetic coercivity, i.e., the resistance of a magnetic material to changes in magnetization (Fig.  4 ). In contrast to large particles or bulk materials, which possess multiple magnetic domain structures, small NPs possess single magnetic domain structures below a certain critical radius (r c ), where all magnetic spins in the NP align unidirectionally (blue arrows in Fig.  4 ). However, the NP radius has to be lower than the threshold radius for superparamagnetism (r sp ) in order to be superparamagnetic [ 89 ]. In the single-domain regime, between r sp and r c , the magnetic coercivity increases as the size of the NP increases until it reaches the maximum at r c [ 84 ]. In this size regime, due to the high magnetic coercivity, the NPs behave similarly as their larger dimension counterparts despite having a single domain structure, i.e., they become ferromagnetic for ferromagnetic materials or paramagnetic for paramagnetic materials etc . Above r c , the magnetic coercivity starts to decrease when multiple magnetic domains are formed in a single NP. The critical radius represents the size where it is energetically favored for the NP to exist without a domain wall [ 86 ]. The calculated critical radii for some common magnetic materials are 35 nm of Ni, 8 nm for Co, and 1 nm for Fe [ 90 ]. Above that point, multi-domain magnetism begins in which a smaller reversal magnetic field is required to make the net magnetization zero [ 84 ].

The change in magnetic coercivity of NPs as a function of particle radius. Figure adapted from Kalubowilage et al., 2019 [ 89 ]. rc critical radius, rsp threshold radius for superparamagnetism

The second key parameter for determining the magnetic properties of NPs is the shape of NPs. In comparison to the size parameter, there is significant less research on the effect of shape on the magnetic properties of NPs having the same volume [ 86 ]. However, large differences in coercivity were found between a set of cubic and spherical CoFe 2 O 4 NPs [ 91 ]. Unlike the curved topography in spherical CoFe 2 O 4 NPs, cubic CoFe 2 O 4 NPs have fewer missing oxygen atoms, and it was hypothesized that this led to less surface pinning and to lower coercivity for the cubic structures [ 86 ]. Other studies also found differences in magnetism between spherical and cubic Fe 3 O 4 NPs [ 92 , 93 ].

Similar to bulk materials, the composition also affects the magnetism of NPs. The magnetocrystalline phase of the NP is significant in determining its magnetic coercivity [ 94 ]. This effect can be observed in magnetic bimetallic core–shell or alloy NPs with anisotropic crystalline structures. For example, Co@Pt core–shell NPs composed of an isotropically structured face-centered cubic Co core and a non-magnetic Pt shell exhibit superparamagnetic behavior with zero coercivity at room temperature [ 95 ]. In general, the compositional modification of NPs by the adoption of magnetic dopants is known to significantly change the magnetism of NPs [ 96 ].

Electronic and optical properties

Metallic and semiconductor NPs possess interesting linear absorption, photoluminescence emission, and nonlinear optical properties due to the quantum confinement and localized surface plasmon resonance (LSPR) effect [ 97 , 98 ]. LSPR phenomena arise when the incident photon frequency is constant with the collective excitation of the conductive electrons [ 25 ].Due to this phenomenon, noble metal NPs exhibit a strong size-dependent UV–visible extinction band that is not present in the spectra of bulk metals. Generally, the optical properties of NPs depend on the size, shape, and the dielectric environment of the NPs [ 99 ].

The collective excitations of conductive electrons in metals are called plasmons [ 100 ]. Depending on the boundary conditions, bulk plasmons, surface-propagating plasmons, and surface-localized plasmons are distinguished (Fig.  5 A–C). Because of their longitudinal nature, the bulk plasmons cannot be excited by visible light. The surface-propagating plasmons propagate along metal surfaces in a waveguide-like fashion [ 98 ]. In the case of NPs, when they are irradiated by visible light, the oscillating electric field causes the conductive electrons to oscillate coherently. When the electron cloud is displaced relative to the nuclei, a restoring force rises from Coulomb attraction between electrons and nuclei that results in oscillation of the electron cloud relative to the nuclear framework [ 99 ]. This creates uncompensated charges at the NP surface (Fig.  5 D). As the main effect producing the restoring force is the polarization of the NP surface, these oscillations are called surface plasmons and have a well-defined resonance frequency [ 98 ].

Graphical illustration of the types of plasmons. A bulk; B surface propagating; and C surface localized plasmons (adapted from Khlebtsov et al., 2010 [ 98 ]). D graphical illustration of the localized surface plasmon resonance (LSPR) in NPs (adapted from Kelly et al., 2003 [ 99 ])

Experimental studies on Ag NPs showed significant differences in their optical properties based on the size of NPs. For Ag NPs with 30 nm radius, the main extinction peak was at 369 nm wavelength, while for Ag NPs with 60 nm radius, a totally different behavior was observed [ 99 ]. The same researchers found that the shape of the NPs also is critical for the optical properties, the plasmon resonance wavelength shifts to the red as the NPs become more oblate [ 99 ], demonstrating that plasmon resonance strongly depend on NPs shape. With respect to the dielectric environment of the NPs, both the surrounding solvent and the support (substrate) were found to be critical for the optical properties. For Ag NPs, both experimental and theorical studies on the effect of surrounding solvent show that plasmon wavelength linearly depends on the refractive index of the solvent [ 99 , 101 ]. At the same time, 10 nm Ag NPs supported on mica substrates displayed LSPR wavelength shifts to the red compared to unsupported NPs [ 102 ]. The biogenic synthesis of NPs can also improve the optical properties. Biologically produced CeO 2 NPs using Simarouba glauca leave extract were found to have different absorption bands and higher band gap energies compared to chemically produced CeO 2 NPs. These superior optical properties were attributed to the better crystallinity and small size of biogenic NPs compared to chemical NPs [ 103 ]. Biogenic NPs can also offer higher photocatalytic activities, e.g., ZnO NPs produced by Plectranthus amboinicus leaf extract had higher photocatalytic activity in the photodegradation of methyl red under UV illumination compared to chemical produced ZnO NPs [ 104 ].

Catalytic properties

Nano-catalysis, i.e., the use of NPs as catalysts, is a quickly evolving field within chemical catalysis. Significantly enhanced or novel catalytic properties such as reactivity and selectivity have been reported for NP catalysts compared to their bulk analogues. The catalytic properties of NPs depend on the size, shape, composition, interparticle spacing, the oxidation state, and the support of the NPs [ 76 ].

The dependency of catalytic activity on the size of NPs is well studied. The relation is an inverse one, i.e., the smaller the NPs the more catalytically active they are. This relationship was found e.g., in the electro-catalysis oxidation of CO by size-selected Au NPs (1.5, 4, and 6 nm) deposited on indium tin oxide. The researchers observed that the smallest NPs provided the highest normalized current densities [ 105 ]. The same relationship was also found in several other studies [ 106 , 107 , 108 , 109 , 110 ]. Goodman et al., 1998 [ 111 ] speculated originally that this behavior could be attributed to quantum-size effects generated by the confinement of electrons within a small volume. Later, size-dependent changes in the electronic structure of the clusters [ 112 ] and the resulting larger number of low-coordinated atoms available for interaction by the larger surface-to-volume ratios with smaller NPs were discussed [ 76 ].

The shape is also known to affect the reactivity and selectivity of the NPs. For the oxidation of CO by Au NPs, hemispherical NPs were found to be more active than spherical ones [ 113 ]. For the oxidation of styrene by Ag NPs, nanocubes were found to be fourteen times more efficient than nanoplates and four times more efficient than nanospheres [ 114 ]. The reason for these dramatical changes are attributed to the increase/decrease in the relative area of the catalytically active surface facets [ 76 ] or to the differences in stability for different NP shapes [ 115 ].

As for composition, several studies have shown that the use of alloys in NPs can enhance the catalytic activity as a result of the alloying effect causing changes in the electronic properties of the catalyst, decreasing poisoning effects, and providing distinct selectivities [ 76 ]. For example, the alloying of Pt with other metals such as Ru, Ni, and Co, was reported to enhance the hydrogenation and oxygen reduction activity of the NP catalyst material, as well as enhancing the resistance against CO poisoning [ 116 , 117 , 118 ]. However, the alloying of Pt with Fe, Ru, and Pd, resulted in reduced reactivity for methanol decomposition [ 119 ]. This reduction in reactivity was explained by the possible occupation of the surface with the addition metal atoms, since pure Fe, Ru, and Pd clusters are less reactive for methanol decomposition than similarly-sized pure Pt clusters. In general, the change in the composition of NPs changes the electronic structure of metal surfaces by the formation of bimetallic bonds as well as the modification of metal–metal bond lengths [ 76 ]. In addition, the charge-transfer phenomenon between different metals may favorably change the binding energy of adsorbents, lower the barriers for specific chemical reactions, and enhance resistance against poisoning [ 120 , 121 , 122 ].

The catalytic activity and stability of 2 nm Au NPs dispersed on polycrystalline TiC films displayed a strong dependence on interparticle spacing. In this study, Au NPs having two different interparticle spacing (30 and 80 nm) were analyzed by Thermal Desorption Spectroscopy. It was found that the sample with smaller interparticle spacing was poisoned and subsequently deactivated while the sample with longer interparticle spacing showed longer lifetime [ 123 ]. At the same time, the oxidation state of NPs was shown to affect the catalytic activities. Ru NPs under rich O 2 conditions and moderate temperatures oxidize and form RuO 2 , the reaction of CO oxidation was found to occur on the metal oxide surface not the metal surface [ 124 ]. A similar effect on CO oxidation was also observed with Pt NPs in which the reactivity of PtO 2 was found to be higher than Pt [ 125 ]. The reaction of CO oxidation was compared for several metal NPs (Ru, Pd, Ir, Os, and Pt) and their corresponding oxides, and the oxides were indeed more reactive than the metals [ 126 , 127 ]. The superior catalytic performance of RuO 2 over their metallic counterparts is generally agreed on, nevertheless, the same cannot be said for other catalytically active metals such as Pt [ 76 ]. In general, these differences in catalytic performance are attributed to the electron transfer processes at the metal/metal oxide interfaces. Consequently, the view that NP oxidation is an undesirable process that leads to the reduction of catalytic performance needs to be reconsidered [ 128 ].

An example for the effect of the support material is the role of the MgO support for Au NPs, where MgO was found to be important for CO oxidation and particularly, for controlling the rate of CO oxidation through oxygen vacancies [ 129 ]. Later, the process of electron charge transfer from oxygen vacancies at the metal-substrate interface of supported Au NPs was suggested to be an ideal environment for O 2 activation and oxidation reactions [ 130 ]. A similar behavior was also found in the decomposition of SO 2 and dissociation of water by Au NPs supported on CeO 2 , in which CeO 2 supports played a critical role [ 131 ]. The experiments showed that not only the chemical composition of the support affects the reactivity of the catalyst, but the crystal structure of the support, too [ 132 ]. Enhanced catalytic performance for CO oxidation and SO 2 dissociation have also been reported for Au NPs supported on metal carbides such as TiC [ 108 , 133 ]. In addition to enhanced catalytic reactivities, the support also plays an important role in NP stabilization [ 106 ], i.e., the stabilization of NPs against coarsening, the stabilization of metal oxides at the NP surface, and the stabilization of intermediate reactions species [ 76 ].

Characterization of NPs

The properties of NPs determine their potential applications. Hence, different methods and techniques are used for the analysis and characterization of the various physicochemical properties of NPs. Table 1 summarizes all characterization techniques mentioned in this review and shows what properties and features can be resolved by each technique.

Morphological and topographical characterization

The morphological and topographical features of NPs are of great interest since they influence most of the properties of NPs as described above. These features include the size, shape, dispersity, localization, agglomeration/aggregation, surface morphology, surface area, and porosity of the NPs. The following techniques are regularly used for the characterization of morphological and topographical features of NPs.

Electron microscopy (EM)

Scanning electron microscopy (SEM), scanning tunneling microscopy (STM), and transmission electron microscopy (TEM) are frequently employed for the analysis of NP size, shape, and surface. In SEM, an electron gun is used to produce a beam of electrons that is controlled by a set of lenses to follows a vertical path through the microscope until it hits the samples. Once the sample is hit by the beam, electrons and X-rays are ejected from the sample. Detectors are then used to collect the X-rays and scattered electrons in order to create a 3D image of the sample. SEM provides different information about the NPs such as size, shape, aggregation, and dispersion [ 134 ]. Similarly, TEM provides information about the size, shape, localization, dispersity, and aggregation of NPs in two-dimensional images [ 25 ]. TEM employs an electromagnetic lens that focuses a very fine beam of electrons into an ultrathin section of the sample. This beam passes through the specimen where the electrons either scatter or penetrate the sample and hit a fluorescent screen at the bottom of the microscope. The difference in electron densities is used for the contrast to create an image of the specimen. TEM can be also used for the characterization of NP crystal structure through the use of selected area electron diffraction (SAED), where the electron beam is focused on a selected area in the sample and the scattered electrons are used to obtain a diffraction pattern. STM is based on the phenomenon of quantum tunneling, where a metallic tip is brough very close to the sample surface and used to apply voltage. When voltage is applied, electrons from the sample surface are extracted creating an electrical current that is used to reconstruct an image of the surface with atomic resolution [ 135 ]. STM is mainly used to characterize the topography of NPs. For inorganic NPs, these techniques offer excellent approaches for the determination of morphological features of NPs. For organic NPs (or NPs coated with biological materials), these techniques require sophisticated sample preparations which constitute major restrictions to their use [ 136 ]. The sample preparation for these techniques might cause sample dehydration, which might lead e.g. to sample shrinking and aggregation [ 136 ].

Examples: TEM was used for the characterization of Ag NPs produced by Arbutus unedo leaf extract. In this example, the NPs have a spherical morphology with a uniform size of 30 nm. The NPs were found to agglomerate into small aggregates, each including 5–6 NPs. At the same time, the SAED approach was used to determine the crystal structure of the NPs. The majority of the NPs were found to be single crystalline cubic materials predominately oriented along their (111) direction [ 137 ]. For the characterization of Ag NPs produced by Diospyros kaki leaf extract, SEM helped to show that the NPs were also spherical and the size was 32 nm with some deviations [ 138 ]. STM is less frequently used for the characterization of biogenic NPs. The features of Ag NPs produced by lime, sweet-lime, and orange juices were compared using STM technique [ 139 ].

Dynamic light scattering (DLS)

This technique is a common approach for the analysis of NP size and size distribution. This approach involves the measurement of light interference based on the Brownian motion of NPs in suspension, and on the correlation of NP velocity (diffusion coefficient) with their size using Strokes-Einstein equation [ 140 ]. The size distribution range of NPs is shown as the polydispersity index, which is the output of an autocorrelation function [ 136 ]. The polydispersity index values lie between 0 and 1, where 0 represents a completely homogenous population and 1 represents a highly heterogeneous population. This technique also allows the analysis of non-spherical NPs through the use of multistage DLS [ 136 ]. This technique is also referred to as photon correlation spectroscopy (PCS) [ 141 ].

Examples: DLS was used to measure the size and the size distribution profile of a wide range of biogenic NPs. The average size of Ag NPs produced by Trichoderma koningii fungi was found to be around 25 nm and the size distribution profile was between 14 and 34 nm. The polydispersity index for those NPs was 0.681, which indicates that they are polydispersed [ 142 ]. While the average size of Ag NPs produced by potato ( Solanum tuberosum ) was found to be around 10–12 nm with a wider distribution profile between 3–65 nm [ 143 ]. In a different application, DLS was employed to study the size increase of biogenic MnO 2 NPs overtime, demonstrating that their size is 7.5 nm after 3 min of the initiation of the reaction, then their size grows overtime until it become 54 nm after 31 min [ 144 ].

Nanoparticle tracking analysis (NTA)

This method is used for the analysis of NP size in suspensions based on their Brownian motion. Like in DLS, the rate of NP movement is correlated with their size using Strokes-Einstein equation, allowing the measurement of size distribution profiles for NPs with 10–1000 nm diameter. Its advantage over DLS is that NP motion is analyzed by video. Individual positional changes of NPs are tracked in two dimensions, which are used to determine NP diffusion rates, and by knowing the diffusion coefficient, the hydrodynamic diameter of the particles can be calculated. In DLS, individual NPs are not visualized, but instead, the time-dependent intensity fluctuations caused by Brownian motion are used to calculate the polydispersity index [ 145 ]. NTA was found to be more precise for sizing monodisperse as well as polydisperse organic NPs compared to DLS [ 146 ].

Examples: NTA was used to measure the size and dispersity of Ag NPs produced by Camellia sinensis (green tea) powder, the NPs were found to be well dispersed in an aqueous medium with an average size of 45 ± 12 nm [ 147 ]. For Se NPs produced by lactic acid bacteria, NTA was employed to measure the size and the concentration of NPs. The average size was found to be 187 ± 56 nm with a concentration of (4.67 ± 0.30) × 10 9 Se NPs per ml [ 148 ].

Brunauer–Emmett–Teller (BET) method

This method is based on the adsorption and desorption principle developed by Stephen Brunauer, Paul Emmett, and Edward Teller, and it is considered one of the best methods for the analysis of NP surface area [ 25 ]. In BET analysis, a partial vacuum is created to produce adsorption between the sample and liquid N 2 (because the interaction between solid and gaseous phases is weak, the surface is cooled with liquid N 2 to obtain detectable amounts of adsorption). After the formation of adsorption monolayers, the sample is removed from the N 2 atmosphere and heated to cause the adsorbed N 2 to be released from the material (desorption) and quantified. The data collected is displayed in the form of isotherms (graphs representing the amount of N 2 adsorbed as a function of relative pressure at a constant temperature). The data is displayed in five isotherms where the information is used to determine the surface area of the sample [ 25 , 149 ]. Figure  6 graphically illustrates the principle of this method.

Principles of the BET and BJH methods. The BET method (steps 1–3) is based on the adsorption of nitrogen on the NP surface. After the formation of a monolayer, nitrogen is desorbed, and the surface area is calculated. The BJH method (steps 1, 2, 4, and 5) is based on the complete filling of NP pores with liquid nitrogen. When saturation is reached, nitrogen is desorbed, and pore size is calculated

Examples: The BET method was employed to measure the surface area of CeO 2 NPs produced by Eucalyptus globulus leaf extract. The surface area was found to be 40.96 m 2 /g of biogenic CeO 2 NPs, much higher than the commercial CeO 2 NPs (8.5 m 2 /g) [ 150 ]. BET was also used to measure the surface area of SiO 2 NPs produced by rice husk, CuO NPs produced by Leucaena leucocephala leaf extract, and Ag NPs produced by Acanthospermum hispidum leaf extract. In these examples, the surface area was 7.15 m 2 /g, 47.54 m 2 /g, and 9.91 m 2 /g, respectively [ 151 , 152 , 153 ].

Barrett–Joyner–Halenda (BJH) method

This method is based on the Barrett–Joyner–Halenda principle and is used for the determination of porosity (or pore size) of NPs. Similar to the BET method, this method also involves the use of N 2 gas to adsorb to the sample. In the BJH method, the process is extended so the gas condensates in the sample pores as pressure increases. The pressure is increased until a saturation point is achieved, at which all the pores of the sample are filled with liquid. Afterwards, the condensated gas is allowed to evaporate where the desorption data is calculated and correlated to the pore size using a modified Kelvin equation (Kelvin model of pore filling) [ 154 , 155 ]. Figure  6 graphically illustrates this method.

Examples: The BJH method was employed to study the pore size of a wide range of biogenic NPs, for instance, the pore size of CeO 2 NPs produced by Eucalyptus globulus leaf extract was found to be 7.8 nm [ 150 ], the pore size of CuO NPs produced by Leucaena leucocephala leaf extract was 2.13 nm [ 152 ], the pore size of SiO 2 NPs produced by rice husk and Ag NPs produced by Acanthospermum hispidum leaf extract were much larger, being 29.63 nm and 36.34 nm, respectively [ 151 , 153 ].

Structural and chemical characterization

The structural characterization of NPs and the study of their composition is of high interest due to the strong influence of these parameters on the physicochemical properties. The following techniques are commonly used for the analysis of NP composition, phase, crystallinity, functionalization, chemical state (oxidation), surface charge, polarity, bonding, and electrochemical properties.

X-ray diffraction analysis (XRD)

This technique is based on irradiating a material with incident X-rays and then measuring the intensities and scattering angles of the X-rays that leave the material [ 156 ]. This technique is widely used for the analysis of NP phase and crystallinity. However, the resolution and accuracy of XRD can be affected in cases where the samples have highly amorphous characteristics with varied interatomic distances or when the NPs are smaller than several hundreds of atoms [ 25 ].

Examples: For the characterization of biogenic Ag NPs, the XRD results of Ag NPs produced by Trichoderma koningii [ 142 ], Solanum tuberosum [ 143 ], and Acanthospermum hispidum leaf extract [ 153 ] displayed characteristic peaks occurring at roughly 2θ = 38 o , 44°, and 64 o corresponding to (111), (200), and (220) planes, respectively. These results are in good agreement with the reference to the face-centered cubic structure of crystalline silver. However, the XRD results of Ag NPs produced by Solanum tuberosum were not as clear as the other biogenic Ag NPs and had several impurities. The structural characterization of Pd NPs produced by Garcinia pedunculata Roxb leaf extract by XRD showed the distinct peaks of Pd, however, three other peaks were also observed at 2θ of 34.22˚, 55.72˚, and 86.38˚, indicating the presence of PdO phases along with Pd NPs [ 157 ].

Energy-dispersive X-ray spectroscopy (EDX)

This technique is based on the irradiation of the sample with an electron beam. Electrons of the electron beam when incident on the sample surface eject inner shell electrons, the transition of outer shell electrons to fill up the vacancy in the inner shell produces X-rays. Each element produces a characteristic X-ray emission pattern due to its unique atomic structure, and therefore can be used to perform compositional analysis [ 158 ]. The shortfall of EDX is that the resulting spectra give only qualitative compositional information (it shows the chemical elements present in the sample without quantification). However, the peak intensities to some extent give an estimate of the relative abundance of an element in a sample [ 159 ]. This technique does not require sophisticated additional infrastructures, usually it is a small device that is connected to an existing SEM or TEM. This allows the use of SEM or TEM for the morphological characterization and EDX is used simultaneously for the analysis of chemical composition [ 160 ].

Examples: The EDX technique is usually used for the confirmation of the presence of the element in question in biogenic NPs. For instance, EDX was used to confirm the presence of Au in Au NPs produced by Jasminum auriculatum leaf extract [ 161 ], the presence of Pd in Pd NPs produced by Pulicaria glutinosa extract [ 162 ], the presence of Te in Te NPs produced by Penicillium chrysogenum PTCC 5031 [ 163 ], and the presence of Ag in Ag NPs produced by Trichoderma viride [ 164 ].

High-angle annular dark-field imaging (HAADF)

This method is used for the elemental mapping of a sample using a scanning transmission electron microscope (STEM). The images are formed by the collection of incoherently scattering electrons with an annular dark-field detector [ 165 ]. This method offers high sensitivity to variations in the atomic number of elements of the sample, and it is used for elemental composition analysis usually when the NPs of interest consist of relatively heavy elements. The contrast of the images is strongly correlated with atomic number and specimen thickness [ 166 ].

Examples: The employment of HAADF-STEM in the characterization of biogenic Au–Ag–Cu alloy NPs confirmed the presence of the three elements in the same NP [ 167 ]. Similarly, this approach revealed that Ag NPs produced by Andrographis paniculata stem extract were coated with an organic polymer [ 168 ]. The employment of this approach in the characterization of Cu NPs produced by Shewanella oneidensis revealed that Cu NPs remained stable against oxidization under anaerobic conditions, but when they were exposed to air a thin shell of Cu 2 O develop around the NPs [ 169 ].

X-ray photoelectron spectroscopy (XPS)

This technique is considered the most sensitive approach for the determination of NP exact elemental ratios, chemical state, and exact bonding nature of NP materials [ 25 ]. XPS is based on the photoelectric effect that can identify the elements within a material, or covering a material, as well as their chemical state with high precision [ 170 ]. XPS can also be used to provide in-depth information on electron transfer, e.g., for Pt NPs supported on CeO 2 , it was found that per ten Pt atoms only one electron is transferred to the support [ 171 ].

Examples: The XPS technique can employed for different purposes. For instance, it was used for measuring the purity of Au NPs produced by cumin seed powder [ 172 ]. XPS was used for the determination of the oxidation states of Pt NPs produced by Nigella sativa seeds and Ag NPs produced by Rosa canina . XPS results of Pt NPs showed the presence of three oxidation states for Pt (Pt (0), Pt (II), and Pt (IV)) and two oxidation states for Ag NPs (Ag (0) and Ag (I)). In both cases, the zero-oxidation state was the abundant one, the presence of a small amount of the other oxidation states suggests that some of the NPs were oxidized or had unreduced species [ 173 , 174 ]. XPS was used for the determination of the exact elemental ratios and the bonding nature of FeS NPs produced by Shewanella putrefaciens CN32. For the exact elemental ratios, the researchers compared biogenic and abiotic FeS NPs and found that biogenic FeS NPs had a 2.3:1 Fe:S ratio while the abiotic NPs had a 1.3:1 Fe:S ratio. For the bonding nature, it was determined that the surface of NPs had Fe(II)-S, Fe(III)-S, Fe(II)-O, and Fe(III)-O bonds [ 175 ].

Fourier-transform infrared spectroscopy (FTIR)

This technique is based on irradiating a material with infrared light, where the absorbed or transmitted radiation is recorded. The resulting spectrum represents a unique fingerprint of samples, where information about the nature of the sample can be obtained such as the bonds involved, polarity, and oxidation state of the sample [ 176 , 177 ]. This technique is mainly used for the characterization of organic materials such as the surface chemical composition or functionalization of NPs. It is also used for the identification of contaminants when high purity is sought [ 178 ].

Examples: For biogenic NPs, FTIR is usually used for the identification of probable functional groups present on the surface of NPs that are responsible for the reduction and stabilization of the NPs. For plant-mediated NP synthesis, for instance for Ag NPs produced by Camellia sinensis , the FTIR results indicate the presence of Camellia sinensis phytocompounds, such as caffeine and catechin, on the surface of Ag NPs that could be responsible for the reduction of Ag or act as stabilizing agents [ 147 ]. For Ag NPs produced by Solanum tuberosum , the NPs were found to be capped by amide and amine groups [ 143 ]. For CeO 2 NPs produced by Eucalyptus globulus , the polyphenol groups present in Eucalyptus globulus extract were found on the surface of NPs suggesting their involvement in the reduction/stabilization process [ 150 ]. For microbe-mediated NP synthesis, FTIR results show the presence of protein residues on the surface of NPs confirming the involvement of different proteins in the reduction/stabilization process, such as in Ag NPs produced by Streptomyces sp. NH28 [ 179 ], in Te NPs produced by Penicillium chrysogenum PTCC 5031 [ 163 ], and in Se NPs produced by Azospirillum thiophilum [ 180 ].

Zeta potential analysis

Zeta potential measurements are used for the determination of NP surface charge in colloidal solutions. The surface charge of NPs attracts counter-ions that form a thin layer on the surface of the NPs (called Stern layer). This layer travels with the NPs as they diffuse thought the solution. The electric potential at the boundary of this layer is known as NP zeta potential [ 136 ]. The instruments used to measure this potential are called zeta potential analyzers [ 181 ]. Zeta potential values are indicative for NP stability, where higher absolute value of zeta potential indicate more stable NPs [ 136 ].

Examples: The zeta potential is a good indicator for the stability of NPs, where NPs with zeta potentials of more than + 30 mV or less than − 30 mV are considered stable. Zeta potentials have been measured for a wide range of biogenic NPs. The zeta potential for Ag NPs produced by Ziziphus jujuba leaf extract of − 26.4 mV [ 182 ]. Ag NPs produced by other organisms have different zeta potential values, for example, Ag NPs produced by Punica granatum peel extract have a zeta potential of − 40.6 mV indicating their higher stability [ 183 ], while Ag NPs produced by Aspergillus tubingensis have a zeta potential of + 8.48 indicating their relative instability [ 184 ]. The pH of the sample is another important parameter for zeta potential values, the higher pH the lower the zeta potential value [ 185 ]. Having different zeta potential values for the same type of NPs depending on the organism used for their synthesis is not unique to silver, Se NPs also show different potential values depending on the organism used for their synthesis [ 186 ].

Cyclic voltammetry (CV)

CV is an electrochemical technique for measuring the current response of redox-active solutions to a linearly cycled potential sweep between two or more set values. The CV technique involves the use of three electrodes: a working electrode, reference electrode, and counter electrode. These electrodes are introduced to an electrochemical cell filled with an electrolyte solution and where voltage is in excess, the potential of the working electrode is cycled and the resulting current is measured. This technique is used for determining information about the reduction potential of materials, the kinetics of electron transfer reactions, and the thermodynamics of redox processes [ 187 , 188 , 189 ].

Examples: The CV technique can be employed for two different purposes in the context of biogenic NP characterization. Firstly, it can be used for measuring the stability of NPs in electrocatalysis. For this purpose, the biogenic NPs are assembled on an electrode of the electrolysis cell and are tested for their electrocatalytic behavior against a redox reaction over different cycles. As an example, Ag NPs produced by Citrus sinensis were found to be stable in phenolic compounds redox reactions over multiple cycles [ 190 ]. Secondly, CV can be used for monitoring the progress of reduction of metallic NPs or for the determination of the reducing agent involved in the reduction. For example, for Ag NPs produced by Indian propolis, four cyclic voltammograms were recorded, one for a water extract of Indian propolis, another for an ethanol extract of Indian propolis, and two for the constituent flavonoids of Indian propolis (pinocembrin and galangin). The four cyclic voltammograms showed similar behaviors indicating the involvement of these flavonoids in the reduction of Ag and in forming Ag NPs [ 191 ].

Raman spectroscopy

This technique is based on irradiating a sample with monochromatic light emitted by a laser, in which the interactions between the laser light and molecular vibrations (photons and phonons) are recorded. The technique records the inelastically scattered photons, known as Raman scattering (named after the Indian physician C. V. Raman) [ 192 ]. The output of this technique is a unique fingerprint for each sample, which is used to characterize the chemical and intramolecular bonding of the sample. It can also be used to characterize the crystallographic orientation of the sample [ 193 ]. Surface-enhanced Raman spectroscopy (SERS) enhances Raman scattering of a sample and provides a more sensitive, specific, and selective technique for identifying molecular structures [ 194 ]. Both techniques are also used for the characterization of optical properties, where the recorded photons and phonons are used to understand the plasmonic resonance of NPs [ 25 ].

Examples: Raman spectroscopy was used to characterize Fe 3 O 4 NPs produced by Pisum sativum peel, the researchers found that the NPs were Fe 3 O 4 NPs with face centered cubic phase which was in agreement with their XRD measurements [ 195 ]. Other researchers used Raman spectroscopy for studying the trace deposits of carbohydrates on ferrihydrite NPs produced by Klebsiella oxytoca , the results showed that the pores of NPs had more deposits of carbohydrates that the surface of the NPs [ 196 ]. For Au NPs produced by Raphidocelis subcapitata (green algae), several biomolecules were suggested for their involvement in this process. SERS technique was used to study Au NPs surface-associated biomolecules in order to narrow down the list of biomolecules involved in the bioproduction process. The researchers found that several biomolecules such as, glutathione, β-carotene, chlorophyll a, hydroxyquinoline, and NAD were associated with Au NPs surface, thus, ruling out other molecules such as, glutaraldehyde fixing agent, saccharides, FAD, lipids, and DNA from the list [ 197 ].

Characterization of optical, electronic, and electrical properties

In addition to Raman spectroscopy and SERS, also other techniques can be employed to study and characterize the optical properties of NPs. These techniques give information about the absorption, reflectance, fluorescence, luminescence, electronic state, bandgap, photoactivity, and electrical conductance properties of NPs.

Ultraviolet–visible spectroscopy (UV–vis) and photoluminescence spectroscopy (PL)

In absorption spectroscopy such as UV–vis, the transition of electrons from the ground state to an excited state is measured, while in photoluminescence spectroscopy, the transition of electrons from the excited state to the ground state is measured [ 198 ]. UV–vis spectroscopy uses visible and UV light to measure the absorption or reflectance of a sample. In photoluminescence spectroscopy, usually UV light is used to excite the electron and then measure the luminescence or fluorescence properties of a sample [ 199 ].

Examples: UV–vis spectroscopy is a simple and common technique that is used for the characterization of the optical properties of NPs. For instance, for the characterization of the optical properties of Ag NPs produced by Trichoderma viride , the UV–vis spectrum showed that a Ag surface plasmon band occurs at 405 nm, which is a characteristic band for Ag NPs. The intensity of this band over the reaction time increased as a result of increasing Ag NP concentration in the solution. In the same study, the photoluminescence properties of these NPs were recorded, with an emission in the range between 320–520 nm, which falls in the blue-orange region [ 164 ]. For biogenic Cu NPs, the common absorption peaks are located between 530–590 nm. The difference in NP size and the bio-active molecules used for the reduction process are believed to be the reasons behind the differences in the absorption peaks [ 200 ]. For instance, 15 nm spherical Cu NPs produced by Calotropis procera have an absorption peak at 570 nm [ 201 ], while 76 nm spherical Cu NPs produced by Duranta erecta have an absorption peak at 588 nm [ 202 ]. The same applies to photoluminescence effects, where 27 nm spherical Cu NPs produced by Tilia extract emit light of 563 nm (dark brown) [ 203 ], while 19 nm spherical Cu NPs emit light of 430 nm (green) [ 204 ].

UV–vis diffuse reflectance spectroscopy (DRS)

This technique uses UV and visible light to measure the diffuse reflectance of a material (the reflection of light in many angles, as opposed to specular reflection). The resulting diffuse reflectance spectra are used to determine the electronic state of a sample, which is then used to calculate the bandgap [ 25 ]. Bandgap determination is crucial for determining conductance and photocatalytic properties especially for semiconductor NPs [ 205 ].

Examples: The DRS technique was used to calculate the bandgap for a wide range of biogenic NPs. For instance, TiO 2 NPs produced by Andrographis paniculata exhibit an optical energy bandgap of 3.27 eV [ 206 ]. Interestingly, biogenic ZnO NPs produced by different organism show different bandgaps, for example, ZnO NPs produced by Pseudomonas putida have a bandgap of 4 eV [ 207 ], while ZnO NPs produced by Calotropis procera leaf extract have a bandgap of 3.1 eV [ 208 ].

Spectroscopic ellipsometry

This technique is based on irradiating a sample with polarized light to measures changes in polarization. It is widely used to calculate the optical constants of a material (refractive index and extinction coefficient) [ 209 ]. This technique is also used to characterize the electrical conductivity and dielectric properties of materials [ 210 ].

Examples: Spectroscopic ellipsometry is not a common technique for the characterization of biogenic NPs. For chemically produced NPs, the optical properties for different-sized Au NPs partially embedded in glass substrate were measured by spectroscopic ellipsometry. In this example, a clear transition from LSPR to SPR mode was found as the thickness increases. Moreover, the partially-embedded Au NPs had much higher refractive index sensitivity compared to Au NPs fully immobilized in a glass substrate [ 211 ]. Spectroscopic ellipsometry was also used to measure the changes in the optical constants of a layer of 5 nm ZnO NPs induced by UV illumination. In this case, it was found that the UV illumination of ZnO NPs in inert atmospheres resulted in a clear blue shift in the absorption (Moss-Burstein shift). The UV illumination of ZnO NPs results in the desorption of O 2 from the NPs surface leading to the population of the lowest levels in conduction band with mobile electrons. This phenomenon is reversible, in which the exposure to O 2 from air results in the scavenging of these mobile electrons [ 212 ].

Characterization of magnetic properties

The magnetic properties of NPs are of high importance, as they potentially give NPs great advantages in catalysis, electronics, and medical applications. Several techniques were developed for the detection and quantification of small magnetic moments in NPs.

Magnetic force microscopy (MFM)

This technique is a variety of atomic force microscopy (AFM), in which a magnetic tip is used to scan the sample. The magnetic tip is approached very close to the sample, where the magnetic interactions between the tip and the sample are recorded [ 213 ]. At closer distances to the sample (0–20 nm), other forces such as van der Waals forces also interact with the tip. Therefore, MFM measurements are often operated with two-pass scanning method (also called lift height method) [ 214 ] (Fig.  7 ). In this method, the tip is firstly used to measure the topography of the sample including the molecular forces as van der Waals. Afterwards, the tip is lifted and a second scan is operated following the same topography outline. In the second scan, the short-ranged van der Waals forces disappear and the long-range magnetic forces are almost exclusively recorded. In an experimental study, researchers found that 22 nm was the optimal scanning height for the second scan, at which van der Waals forces are very weak while the distance is still small enough to measure the magnetic interactions for Pd-Fe bimetallic NPs [ 215 ].

Magnetic force microscopy lift height method. The first scan is done very close to the surface to obtain the topography of the sample. Then, the tip is lifted and a second scan is performed following the topography outline obtained in the first scan

Examples: MFM was heavily used for the characterization of magnetite NPs produced by magnetotactic bacteria. For instance, the size and orientation of the magnetic moment of magnetite NPs produced by Magnetospirillum gryphiswaldense strain MSR-1 were studied by MFM [ 216 ], in which the size of the magnetic moment was found to be 1.61 × 10 −17 Am 2 . In a different study, MFM was used to characterize the magnetic properties and to estimate the size of the magnetic kernel of the magnetosomes produced by the same strain, and it was determined that the NPs behaved like single mono-domain nanomagnets [ 217 ]. The magnetic properties of NPs made from materials such as Pd that only exhibit significant magnetism on the nanoscale can also be studied by MFM, however, the magnetic moment of these NPs is much lower than for ferromagnetic NPs. The magnetic decoration of Pd NP samples with Fe 2 O 3 NPs strongly enhances the weak magnetic signal of Pd NPs up to 15 times [ 218 ]. This approach could make the MFM technique useful for the characterization of weak magnetic NPs.

Vibrating-sample magnetometry (VSM)

This technique measures the magnetic properties of materials based on Faraday’s law of induction. In VSM, the sample is placed in a constant magnetic field in a special holder that vibrates vertically. As the holder starts vibrating, the magnetic moment of the sample creates a magnetic field that changes as function of time. The alternating magnetic field created in the sample induces an electric current that is recorded and used to calculate the magnetic properties of the sample [ 219 , 220 ].

Examples: For the characterization of Fe 2 O 3 NPs produced by Tridax leaf extract, VSM studies revealed that the NPs had a saturation magnetization of 7.78 emu/g, a remnant magnetization of 0.054 emu/g, and a coercivity of − 1.6 G [ 221 ]. In other studies, VSM was used to compare the magnetic properties of iron oxide NPs produced Moringa oleifera with the magnetic properties of the same NPs but coated with chitosan. The researchers found that saturation magnetisation, remnant magnetization, and coercivity have lower values when the NPs are coated with chitosan [ 222 ].

Superconducting quantum interference device (SQUID) magnetometry

This technique measures the magnetic properties of materials based on the Josephson effect. Niobium (Nb) or other metal alloys are used in the device which needs to be operated at temperatures very close to the absolute zero to main superconductivity, where liquid helium is used to maintain the cold environment [ 223 ]. However, other kinds of SQUID also exist where high-temperature superconductors are used [ 224 ]. After reaching superconducting environments, the Josephson junctions contained in the device help to create a supercurrent, which is recorded and used to calculate the magnetic properties of the sample [ 225 ].

Examples: For the characterization of iron oxide NPs produced by Cnidium monnieri seed extract, SQUID magnetometry revealed that the NPs had a saturation magnetization of 54.60 emu/g, a remnant magnetization of 1.15 emu/g, a coercivity of 11 Oe, and a magnetic susceptibility of + 1.69 × 10 –3 emu/ cm 3 ⋅ Oe at room temperatures, indicating the superparamagnetic behaviour of these NPs [ 226 ]. SQUID magnetometry was also used for the characterization of the magnetic properties of zinc incorporated magnetite NPs produced by Geobacter sulfurreducens , showing that the loading of only 5% zinc results in the enhancement of saturation magnetization of the NPs by more than 50% [ 227 ].

Electron spin resonance spectroscopy (ESR)

This technique measures the magnetic properties of materials by characterizing and quantifying the unpaired electrons in the sample. Electrons are charged particles that spin around their axis, which can align in two different orientations (+ ½ and − ½) when the sample is placed in strong magnetic field. These two alignments have different energies due to the Zeeman effect. Since unpaired electrons can change their spins by absorbing or emitting photons, in ESR the sample is irradiated with microwave pulses to excite electron spins until a resonance state is reached [ 228 ]. This technique is also referred to as electron paramagnetic resonance spectroscopy (EPR). It can be used to measure the ferromagnetic and antiferromagnetic properties of NPs [ 229 , 230 ].

Examples: ESR was used to characterize the magnetic properties of iron oxide NPs produced by Ficus carica . The trees naturally produce iron oxide NPs as a defence mechanism when are they are subjected to stress. The researchers found that the magnetic properties of iron oxide NPs produced by the same tree but grown in different environmental conditions have different magnetic properties. In addition, a magnetic anisotropy of the signal was visible as the magnetic properties of these NPs varied strongly at different temperatures [ 231 ]. ESR was also used to characterize the magnetic properties of Se nanomaterials produced by anaerobic granular sludge. The ESR results revealed the presence of Fe(III) atoms incorporated in the Se nanomaterial, which enhanced their overall magnetic properties, giving it ferromagnetic behaviour [ 232 ].

Characterization of thermal properties

Several techniques can be used for the characterization of the thermal properties of NPs, such as melting points, crystallization and structural-phase transition points, heat capacity, thermal conductivity, and thermal and oxidative stability.

Differential scanning calorimetry (DSC)

In this technique the analyte and a well-defined reference sample are put at the same temperature, then, the amount of heat required to increase the temperature of the sample and the reference in measured as a function of temperature. This technique is widely used to measure melting points [ 233 ], crystallization points, structural-phase transition points [ 234 ], latent heat capacity [ 235 ], heat of fusion [ 236 ], and oxidative stability [ 237 ].

Examples: For the characterization of Ag NPs produced by Rhodomyrtus tomentosa leaf extract, DSC showed three exothermic peaks at 44, 159, 243, and an endothermic peak at 441 °C. The first peak (at 44 °C) indicates that at this temperature the NPs face a gradual loss of water from their surface. The second peak (at 159 °C) shows that the thermal decomposition of the sample happens at this temperature. The last temperature (441 °C) indicates the melting temperature for those NPs [ 238 ]. For Ag NPs produced by Parthenium hysterophorus leaf extract, DSC showed that their melting temperature was at 750 °C. The researchers also found that these NPs had completely thermally decomposed and crystallized simultaneously [ 239 ].

Differential thermal analysis (DTA)

This technique is based on heating or cooling a sample and an inert reference under identical conditions, where any temperature difference between the sample and the reference is recorded. This technique is primarily used for the study of phase diagrams and transition temperatures [ 240 ]. However, it is also used to measure the melting points, thermal, and oxidative stability [ 241 , 242 ].

Thermogravimetric analysis (TGA)

This technique measures the change in the mass of a sample as a function of temperature and/or time in a controlled atmosphere [ 243 ]. This technique is mainly used to study the thermal stability of materials [ 244 ], in addition, it is also used to measure structural-phase transition points [ 245 ], thermal activation energies [ 246 ], and oxidative stability [ 247 ]. The resulting thermogram is unique for each compound and therefore can also be used for the determination of material composition [ 248 ]. TGA and DTA are usually combined in the same thermal analyzing instrument, called thermogravimetry/differential thermal analysis (TG/DTA) [ 244 ].

Examples: TG/DTA is a common technique for the characterization of thermal properties of biogenic NPs. For instance, the thermal properties of Ag NPs produced by Daphne mucronate leaf extract were studied in the range between 0–1000 °C where the sample was heated at a rate of 10 °C/min. The researchers found that between 400–500 °C the NPs faced a dominant weight loss, while the weight loss below 400 °C and above 500 °C was negligible. The DTA curve showed an intense exothermic peak in the range between 400–500 °C, this indicates that the crystallization of NPs happens in this temperature interval. Some minor weight loss events were seen below 400 °C, this may be caused by the evaporation of water or the degradation of the organic components [ 249 ]. In another study, the thermal properties of Ag NPs produced by two different plants ( Stereospermum binhchauensis and Jasminum subtriplinerve ) were compared. The researchers found that the major weight loss happens between 220–430 °C, which is attributed to the decomposition of biomolecules from the NP surface [ 250 ]. This shows that Ag NPs produced by these plants have much higher content of biomolecules on their surface than Ag NPs produced by Daphne mucronate. TG/DTA showed that Stereospermum binhchauensis Ag NPs crystallize at 315 °C and Jasminum subtriplinerve Ag NPs at 345 °C, around 100 °C less than Daphne mucronate Ag NPs [ 250 ].

Transient hot wire method (THW)

This method is used for the determination of thermal conductivity based on increasing the temperature of a material by a thin hot wire as a function of time, where the heating wire is located directly in the test sample. The advantage of this method over other thermal conductivity measurement methods is the very short measuring time, this gives high accuracy of thermal conductivity due to the negligible values of convection in such short times [ 251 ]. In this method, the NPs are added to a solution (usually water or ethylene glycol) forming a colloidal dispersion called a nanofluid. Then, the thermal conductivity of the nanofluid is measured and compared to the thermal conductivity of the base fluid, giving a thermal conductivity ratio which is used to evaluate the thermal conductivity of different NPs.

Examples: The thermal conductivity ratios of three different concentrations (0.12, 0.18, and 0.24%) of biogenic SnO 2 NPs produced by Punica granatum seed extract were measured in ethylene glycol at 303 K. The researchers found a linear relationship between NPs concentration and the thermal conductivity. The thermal conductivity enhancement of nanofluid to base fluid was between 6 and 24% [ 252 ]. In another study, the thermal conductivity of Fe 2 O 3 NPs produced by Psidium guajava leaf extract was measured in water and in ethylene glycol. The researchers found that the thermal conductivity enhancement in ethylene glycol was better than in water, the thermal conductivity enhancement for 0.025% Fe 2 O 3 NPs in water was 30% while in ethylene glycol was 34%. Moreover, the linear relationship between NPs concentration and thermal conductivity ratio was found for Fe 2 O 3 NPs in both water and ethylene glycol [ 253 ].

Characterization of mechanical properties

Several methods can be used for the characterization of mechanical properties of NPs, such as tensile and compressive strengths, elasticity, viscoelasticity, hardness, and stiffness.

Tensometery

The machine used for this method is called a universal testing machine (UTM) or a tensometer. It is used to measure the elasticity (elastic modulus), tensile and compressive strengths (Young’s modulus) of materials. In this machine, the sample is placed between grips and an extensometer, where changes in gauge length are recorded as a function of load [ 254 ]. However, other mechanical changes in addition to the change in gauge length are also recorded in this machine, such as the elasticity.

Examples: The mechanical properties of different biogenic NP-containing composites can be measured by this machine. For example, the mechanical properties of orthodontic elastic ligatures containing Ag NPs produced by Heterotheca inuloides were studied by comparing the maximum strength, tension, and displacement of the composite with and without the biogenic NPs. The researchers found that maximum strength, tension, and displacement have improved after the addition of Ag NPs [ 255 ]. Interestingly, the addition of biogenic Ag NPs produced by Diospyros lotus fruit extract to starch and polyvinyl alcohol hydrogel membranes resulted in an adverse effect. The tensile strength and modulus of the hydrogel membranes containing 50 and 100 ppm Ag NPs were much lower than of the neat hydrogel membrane. The researchers attributed this adverse effect to the possibility that the addition of Ag NPs could have resulted in blocking the crosslinking between starch and polyvinyl alcohol, or to the possibility of the formation of breakage points in the polymer matrix due to NPs agglomeration [ 256 ].

Instrumented indentation testing

This method is used to characterize the hardness features of materials by using a well-defined hard indenter tip typically made of diamond. The indenter tip is used to make an indentation in the sample by placing incremental loads on the tip, after which the area of indentation in the sample is measured and used to calculate the hardness features [ 257 ]. Light microscopy, SEM, or ATM technique are usually used to visualize the indentation in the sample. The method is also called micro- or nano-indentation testing.

Examples: This method was used to characterize the mechanical properties of calcite NPs produced by Ophiocoma wendtii brittlestar. The arm plates of this brittlestar are covered by hundreds of nanoscale calcite lenses that focus light onto photoreceptor nerve bundles positioned beneath the brittlestar. The researchers used the nanoindentation method to compare Young’s modulus, hardness and fracture toughness of biogenic calcite with geocalcite. The results showed that the biogenic calcite lenses have higher hardness and fracture toughness compared to geocalcite (more than twofold) [ 258 ]. Bamboo is well known for its high silica content in comparison to other wood species. It produces SiO 2 NPs and deposits it in its epidermis in the form of silica cells. The mechanical properties of silica cells compared to other types of cells of Moso bamboo ( Phyllostachys pubescens ) were studied by instrumented indentation testing. The researchers found that the cell wall of silica cells display higher hardness and elastic recovery compared to fibre and epidermal cells, which is attributed to the presence of biogenic SiO 2 NPs in the silica cells [ 259 ].

Dynamic mechanical analysis (DMA)

This method is used to study the mechanical properties of materials by measuring the strain of a material after applying a stress. This method helps to obtain three different values: storage modulus, loss modulus, and loss tangent. These values are important to give an overview about the stiffness and viscoelasticity behavior of materials [ 260 ].

Examples: The DMA method was used to characterize the mechanical properties of polymethyl methacrylate denture base polymer filled with Ag NPs produced by Boesenbergia rotunda . In this study frequency sweep test was used to determine the viscoelastic behavior of this nanocomposite where the temperature was constant at 37 °C and the frequency was increasing from 0.5 to 100 Hz in tension mode. The researchers found a frequency dependence for storage modulus, loss modulus, and loss tangent for the nanocomposite with various Ag NPs loading concentrations. The frequency dependence of storage modulus, loss modulus, and loss tangent indicates the viscoelastic response of this polymer. However, the results showed that the storage modulus for the nanocomposite is much higher than the loss modulus over the range of frequencies, indicating the elastic dominance of the nanocomposite. Moreover, the researchers found that storage and loss moduli increase with increasing Ag NPs loading concentrations, which is due to the interaction between polymethyl methacrylate and Ag NPs [ 261 ].

In a different study, DMA was used to determine the thermomechanical properties of pol(S-co-BuA) polymer filled with cellulose nanocrystals produced by Posidonia oceanica . In this case, the behaviour of storge modulus and loss tangent were studied as a function of temperature for different cellulose nanocrystals loading concentrations. The results showed that the unloaded polymer behaves like an amorphous polymer, the storage modulus remains constant until the temperature reaches 25 °C then it starts to sharply decrease due to glass–rubber transition. A relaxation process was also evident for the unloader polymer, where the loss tangent reaches its maximum at 35 °C then it starts to fall. The addition of cellulose nanocrystals to the polymer positively enhanced both effects. The dramatic drop of storage modulus at 25 °C was less for the nanocomposite, where the drop for the polymer loaded with 15% cellulose nanocrystals was almost cancelled. Similar positive enhancement was found for loss tangent. These enhancements could be attributed to the mechanical coupling effect, in which the NPs connect and form a stiff continuous network linked through hydrogen bonding [ 262 ].

Applications of NPs

NPs, due to their above-mentioned unique or enhanced physicochemical properties, are used in a wide range of applications in different fields. In addition, several potential applications are in research and development. Here we present some examples of these applications.

Applications in medicine and pharma

Metallic and semiconductor NPs have huge potential for cancer diagnosis and therapy based on their enhanced light scattering and absorption properties due to LSPR effect. For instance, Au NPs efficiently absorb light and convert it into localized heat, which can be exploited for selective photothermal therapy of cancer (cancer cell death by heat generated in tumor tissue) [ 263 , 264 ]. In addition, the unique optical properties of Au NPs make them a great candidate for the photodynamic therapy of cancer (the use of a drug that is activated by light to kill cancer cells) [ 265 ]. Gd based NPs have also shown great abilities in tumor growth inhibition [ 266 ], metastasis inhibition [ 267 ], and tumor-specific magnetic resonance contrast enhancement [ 268 ]. Targeted drug delivery is also an important potential application of NPs. ZnO and Fe 3 O 4 NPs were efficiently used for targeted drug delivery and selective destruction of tumor cells [ 269 , 270 , 271 ].

Moreover, NPs have been successfully used in different medical applications such as cellular imaging [ 272 ], or in biosensors for DNA, carbohydrates, proteins, and heavy metal ions [ 273 , 274 ], determination of blood glucose levels [ 275 ], and for medical diagnostics to detect bacteria [ 276 ] and viruses [ 277 ]. For instance, Au NPs were conjugated with SARS-CoV-2 antigens to rapidly detect the presence of SARS-CoV-2 IgM/IgA antibodies in blood samples within 10–15 min [ 278 ], At the same time, due to their antimicrobial and antibacterial activities, NPs such as TiO 2 , ZnO, CuO, and BiVO 4 are being increasing used in various medical products such as catheters [ 279 , 280 ].

Applications in electronics

NPs, due to their novel electronic and optical properties, have a wide range of potential applications in imaging techniques and electronics. For instance, Gd-based NPs can improve the imaging quality and the contrast agent administration dose of magnetic resonance imaging (MRI). The use of Gd 2 O 3 NPs as a contrasting agent was found to be more efficient than the commonly used agent (Gd-DOTA) at the same concentration [ 281 ]. At the same time, GdPO 4 NPs were successfully used for tumor detection using MRI in 1/10 of the dose typically used with Gd-DTPA agent [ 282 ]. Interestingly, NPs also offer the ability to image and track a single molecule, which can reveal important information about cellular processes such as membrane protein organization and interaction with other proteins. For example, Eu 3+ -doped oxide NPs were used to track a single toxin receptor with a localization precision of 30 nm [ 283 ].

Regarding applications in batteries, an important component in lithium-ion batteries is the separators. Their main function is to prevent the physical contact of anode and cathode, and to provide channels for the transport of ions. The commonly used commercial material in battery separators, a polyolefin microporous membrane, suffers from poor electrolyte uptake and poor thermal stability [ 284 ]. Due to the aerogel structure of some NPs (such as ZnO NPs), they are an ideal choice for separator plates in batteries [ 284 ]. This makes the batteries store a significantly higher amount of energy compared to traditional batteries. For lithium-air batteries, using Pt-Au bimetallic NPs strongly enhances oxygen reduction and oxygen evolution reactions [ 285 ]. Moreover, batteries made of nanocrystalline Ni and metal hydrides last longer and require less charging [ 23 ]. In addition to battery applications, several NPs such as CdS and ZnSe are also used in light-emitting diodes (LED) of modern displays to get higher brightness and bigger screens [ 23 , 286 ]. Other NPs such as CdTe NPs are also used in liquid crystal displays (LCDs) [ 287 ]. The addition of a NP layer to LED and LCD enables them to generate more light using the same amount of energy and enhances their lifetime.

Applications in agriculture

NPs have potential to benefit the agriculture field by providing new solutions to current agricultural and environmental problems [ 288 ]. NPs are mainly used in two forms in agriculture, as nanofertilizers and nanopesticides. Chemical fertilizers have poor efficiency due to leaching and volatilization. In these cases, the farmers usually react by using excessive amounts of fertilizers, which increases crops productivity but has an environmental cost [ 288 ]. In contrast, nanofertilizers are compounds that are applied in smaller amounts than regular chemical fertilizers but yet have better efficiencies [ 289 ]. The difference in efficiency comes from the fact that they are able to release the nutrients just when and where they are required by the plants. In that way, they limit the conversion of excess amounts of fertilizer to gaseous forms or from leaking into the ground water [ 290 ]. Several NPs have been employed in the development of fertilizers, including SiO 2 , ZnO, CuO, Fe, and Mg NPs [ 291 , 292 , 293 ]. These nanofertilizers provide the plants with increased nitrogen fixation, improved seed germination, amelioration to drought stress, increased seed weight, and increased photosynthesis ability [ 291 , 292 , 293 ]. The large surface area and small size of these NPs are the main reasons for the better efficiencies of nanofertilizers over conventional fertilizers [ 294 ].

Several NPs have proven antimicrobial, insecticidal, and nematicidal activities, which makes them a promising alternative to chemical pesticides and a potentially cheaper alternative to biopesticides [ 294 ]. For instance, the photocatalytic activity of TiO 2 NPs gives them a potent antimicrobial activity against Xanthomonas perforans , the causing agent of tomato spot disease [ 295 ]. CuO NPs show insecticidal activity against Spodoptera littoralis , known as African cotton leafworm [ 296 ]. Ag NPs show nematicidal activity against Meloidogyne spp. , root-knot nematodes [ 297 ].

Applications in the food industry

NPs, despite toxological concerns, have impactful applications in several food industry-related process such as food production, preservation, and packaging. TiO 2 NPs are a major promising player in this industry. Their photocatalytic antimicrobial activity makes them an interesting material for food packaging [ 298 ]. In addition, they are also used in sensors to detect volatile organic compounds [ 299 ]. Ag NPs are also promising in food packaging due to their antimicrobial activity. They play an important role in reducing the risk of pathogens and extending food shelf-life [ 294 ]. The efficiency of doping Ag and ZnO NPs to degradable and non-degradable packaging materials for meat, bread, fruit, and dairy products was tested against several yeast, molds, aerobic, and anaerobic bacteria [ 300 ]. For instance, polyvinyl chloride doped with Ag NPs was evaluated for packing minced meet at refrigerator temperature (4 °C); the results showed that Ag NPs significantly helped to slow down bacterial growth, increasing the shelf-life of minced meet from 2 to 7 days [ 301 ].

Effects of NPs on biological systems

Although the use of NPs is exponentially growing, their possible toxicological and hazardous impacts to human health and environment cannot be ignored. NPs may get released to the environment during production stages, usage, recycling, or disposal. These NPs may persist in air, soil, water, or biological systems [ 302 ]. NPs can enter the human or animal body though the skin, orally, or via the respiratory tract, and afterwards move to other parts of the body. The exposure to NPs was found to activate proinflammatory cytokines and chemokines with recruitment of inflammatory cells, which impacts the immune system homeostasis and can lead to autoimmune, allergic, or neoplastic diseases [ 302 ]. Moreover, the exposure to ultrafine particles can cause pulmonary, cardiac, and central nervous system diseases [ 303 , 304 , 305 ]. Similarly, NPs can enter plants cells and cause harmful effects [ 306 ]. For instance, the exposure of ZnO and Al NPs was found to cause root growth inhibition in plants [ 307 , 308 ].

Nanoscience and nanotechnology are inherently transdisciplinary fields of science. With new bio-based approaches, there is a need for biologists to understand not only the basic principles of nanoscience, but also the technologies and methods traditionally employed to characterize nanomaterials. We hope that this review can help to inspire new collaborations across different scientific disciplines, by helping biologists to identify the best technologies—and partners—to characterize their nanomaterials. At the same time, we recommend to take potential biological risks of these new materials into careful consideration already during the planning phase of such experiments.

Availability of data and materials

Not applicable.

https://www.etymonline.com/word/nano .

[SOURCE: ISO/TS 80,004‑2:2015, 4.4].

Abbreviations

Atomic force microscopy

Brunauer–Emmett–Teller

Barrett–Joyner–Halenda

Cyclic voltammetry

Dynamic light scattering

Derjaguin–Landau–Verwey–Overbeek

Dynamic mechanical analysis

Derjaguin–Muller–Toporov

UV–vis diffuse reflectance spectroscopy

Differential scanning calorimetry

Differential thermal analysis

Energy-dispersive X-ray spectroscopy

Electron microscopy

Electron paramagnetic resonance spectroscopy

Electron spin resonance spectroscopy

Fourier-transform infrared spectroscopy

High-angle annular dark-field imaging

International Organization for Standardization

Johnson–Kendall–Roberts

Liquid crystal display

Light-emitting diode

Localized surface plasmon resonance

Magnetic force microscopy

Magnetic resonance imaging

Nanoparticles

Nanoparticle tracking analysis

Photoluminescence spectroscopy

Critical radius

Threshold radius for superparamagnetism

Selected area electron diffraction

Scanning electron microscopy

Surface-enhanced Raman spectroscopy

Surface plasmon resonance

Superconducting quantum interference device

Scanning transmission electron microscopy

Scanning tunneling microscopy

Transmission electron microscopy

Thermogravimetry/differential thermal analysis

Thermogravimetric analysis

Transient hot wire

Universal testing machine

Ultraviolet

Ultraviolet–visible spectroscopy

Vibrating-sample magnetometry

X-ray photoelectron spectroscopy

X-ray diffraction analysis

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Joudeh, N., Linke, D. Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists. J Nanobiotechnol 20 , 262 (2022). https://doi.org/10.1186/s12951-022-01477-8

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Nanomaterials: a review of synthesis methods, properties, recent progress, and challenges

First published on 24th February 2021

Nanomaterials have emerged as an amazing class of materials that consists of a broad spectrum of examples with at least one dimension in the range of 1 to 100 nm. Exceptionally high surface areas can be achieved through the rational design of nanomaterials. Nanomaterials can be produced with outstanding magnetic, electrical, optical, mechanical, and catalytic properties that are substantially different from their bulk counterparts. The nanomaterial properties can be tuned as desired via precisely controlling the size, shape, synthesis conditions, and appropriate functionalization. This review discusses a brief history of nanomaterials and their use throughout history to trigger advances in nanotechnology development. In particular, we describe and define various terms relating to nanomaterials. Various nanomaterial synthesis methods, including top-down and bottom-up approaches, are discussed. The unique features of nanomaterials are highlighted throughout the review. This review describes advances in nanomaterials, specifically fullerenes, carbon nanotubes, graphene, carbon quantum dots, nanodiamonds, carbon nanohorns, nanoporous materials, core–shell nanoparticles, silicene, antimonene, MXenes, 2D MOF nanosheets, boron nitride nanosheets, layered double hydroxides, and metal-based nanomaterials. Finally, we conclude by discussing challenges and future perspectives relating to nanomaterials.

1. Introduction

The term nanometer was first used in 1914 by Richard Adolf Zsigmondy. 5 The American physicist and Nobel Prize laureate Richard Feynman introduced the specific concept of nanotechnology in 1959 in his speech during the American Physical Society's annual meeting. This is considered to be the first academic talk about nanotechnology. 5 He presented a lecture that was entitled “There's Plenty of Room at the Bottom”. During this meeting, the following concept was presented: “why can’t we write the entire 24 volumes of the Encyclopedia Britannica on the head of a pin?” The vision was to develop smaller machines, down to the molecular level. 6,7 In this talk, Feynman explained that the laws of nature do not limit our ability to work at the atomic and molecular levels, but rather it is a lack of appropriate equipment and techniques that limit this. 8 Through this, the concept of modern technology was seeded. Due to this, he is often considered the father of modern nanotechnology. Norio Taniguchi might be the first person who used the term nanotechnology, in 1974. Norio Taniguchi stated: “nano-technology mainly consists of the processing of, separation, consolidation, and deformation of materials by one atom or one molecule.” 5,9 Before the 1980s, nanotechnology remained only an area for discussion, but the concept of nanotechnology was seeded in the minds of researchers with the potential for future development.

The invention of various spectroscopic techniques sped up research and innovations in the field of nanotechnology. IBM researchers developed scanning tunneling microscopy (STM) in 1982, and with STM it became feasible to attain images of single atoms on “flat” ( i.e. , not a tip) surfaces. 10 Atomic force microscopy (AFM) was invented in 1986, and it has become the most crucial scanning probe microscope technique. 11 The motivation to develop hard discs with high storage density stimulated the measurement of electrostatic and magnetic forces. This led to the development of Kelvin-probe-, electrostatic-, and magnetic-force microscopy. 12 Currently, nanotechnology is rapidly evolving and becoming part of almost every field related to materials chemistry. The field of nanotechnology is evolving every day, and now powerful characterization and synthesis tools are available for producing nanomaterials with better-controlled dimensions.

Nanotechnology is an excellent example of an emerging technology, offering engineered nanomaterials with the great potential for producing products with substantially improved performances. 13 Currently, nanomaterials find commercial roles in scratch-free paints, surface coatings, electronics, cosmetics, environmental remediation, sports equipment, sensors, and energy-storage devices. 14 This review attempts to provide information in a single platform about the basic concepts, advances, and trends relating to nanomaterials via covering the related information and discussing synthesis methods, properties, and possible opportunities relating to the broad and fascinating area of nanomaterials ( Scheme 1 ). It is not easy to cover all the literature related to nanomaterials, but important papers from history and the current literature are discussed in this review. This review provides fundamental insight for researchers, quickly capturing the advances in and properties of various classes of nanomaterials in one place.

2. Descriptions of terms associated with nanomaterials

3. approaches for the synthesis of nanomaterials, 3.1. top-down approaches.

The conditions under which arc discharge takes place play a significant role in achieving new forms of nanomaterials. The conditions under which different carbon-based nanomaterials are formed via the arc discharge method are explained in Fig. 6 . Various carbon-based nanomaterials are collected from different positions during the arc discharge method, as their growth mechanisms differ. 44 MWCNTs, high-purity polyhedral graphite particles, pyrolytic graphite, and nano-graphite particles can be collected from either anode or cathode deposits or deposits at both electrodes. 46–48 Apart from the electrodes, carbon-based nanomaterials can also be collected from the inner chamber. Different morphologies of single-wall carbon nanohorns (SWCNHs) can be obtained under different atmospheres. For example, ‘dahlia-like’ SWCNHs are produced under an ambient atmosphere, whereas ‘bud-like’ SWCNHs are generated under CO and CO 2 atmospheres. 49 The arc discharge method can be used to efficiently achieve graphene nanostructures. The conditions present during the synthesis of graphene can affect its properties. Graphene sheets prepared via a hydrogen arc discharge exfoliation method are found to be superior in terms of electrical conductivity and have good thermal stability compared to those obtained via argon arc discharge. 50

3.2. Bottom-up approaches

The hard template method is also called nano-casting. Well-designed solid materials are used as templates, and the solid template pores are filled with precursor molecules to achieve nanostructures for required applications ( Fig. 10 ). 78 The selection of the hard template is critical for developing well-ordered mesoporous materials. It is desirable that such hard templates should maintain a mesoporous structure during the precursor conversion process, and they should be easily removable without disrupting the produced nanostructure. A range of materials has been used as hard templates, not limited to carbon black, silica, carbon nanotubes, particles, colloidal crystals, and wood shells. 85 Three main steps are involved in the synthetic pathway for obtaining nanostructures via templating methods. In the first step, the appropriate original template is developed or selected. Then, a targeted precursor is filled into the template mesopores to convert them into an inorganic solid. In the final step, the original template is removed to achieve the mesoporous replica. 86 Via using mesoporous templates, unique nanostructured materials such as nanowires, nanorods, 3D nanostructured materials, nanostructured metal oxides, and many other nanoparticles can be produced. 87 From this brief discussion, it can be seen that a wide range of unique structured nanomaterials can be produced using soft and hard template methods.

4. Unique nanomaterial features

The electronic properties of semiconductors in the 1–10 nm range are controlled by quantum mechanical considerations. Thus, nanospheres with diameters in the range of 1–10 nm are known as quantum dots. The optical properties of nanomaterials such as quantum dots strongly depend upon their shape and size. 96 A photogenerated electron–hole pair has an exciton diameter on the scale of 1–10 nm. Thus, the absorption and emission of light by semiconductors could be controlled via tuning the nanoparticle size in this range. However, in the case of metals, the mean free path of electrons is ∼10–100 nm and, due to this, electronic and optical effects are expected to be observed in the range of ∼10–100 nm. The colors of aqueous solutions of metal nanoparticles can be changed via changing the aspect ratio. Aqueous solutions of Ag NPs show different colors at different aspect ratios. A red shift in the absorption band appears with an increase in the aspect ratio ( Fig. 12 ). 21

Among a range of unique properties, the following key properties can be obtained upon tuning the sizes and morphologies of nanomaterials.

4.1. Surface area

4.2. magnetism, 4.3. quantum effects, 4.4. high thermal and electrical conductivity, 4.5. excellent mechanical properties, 4.6. excellent support for catalysts, 4.7. antimicrobial activity.

Overall, these features have made nanoscale materials valuable for a wide range of applications, substantially boosting the performances of various devices and materials in a number of fields. Details of various nanomaterials, their properties, and applications in various fields will be discussed below.

5. Nanomaterials, characteristics, and applications

5.1. special carbon-based nanomaterials.

In the carbon-based nanomaterial family, fullerenes were the first symmetric material, and they provided new perspectives in the nanomaterials field. This led to the discovery of other carbon-based nanostructured materials, such as carbon nanotubes and graphene. 110 Fullerenes are present in nature and interstellar space. 111 Interestingly, fullerenes were the molecule of the year in 1991 and attracted the most research projects compared to other scientific subjects during that period. 112 Fullerenes possess several unique features that make them attractive for applications in different fields. Fullerenes display solubility to some extent in a range of solvents, and these characteristics make them unique compared to the other allotropes of carbon. 108

The chemical modification of fullerenes is an exciting subject, improving their effectiveness for applications. There are two main ways to modify fullerenes: 113 fullerene inner-space modification, and fullerene outer-surface modification.

Endohedral and exohedral doping examples are shown in Fig. 13 . 114 Fullerenes are hollow cages, and the interior acts as a robust nano-container for host target species when forming endohedral fullerene. 115 Endohedral fullerenes do not always follow the isolated pentagon rule (IPR). 116 To date, fullerene nanocages have received substantial consideration in the materials chemistry field due to their useful potential applications. Neutral and charged single atoms in free space are highly reactive and unstable. In the confined environment of fullerenes, these reactive species can be stabilized; for example, the LaC 60 + ion does not react with the NH 3 , O 2 , H 2 , or NO. Thus, reactive metals can be protected from the surrounding environment by trapping them inside fullerene cages. 117 Another emerging carbon nanomaterial is endohedral fullerene containing lithium (Li@C 60 ). 118 Lithium metal is very reactive, and extreme controlled environmental conditions are required to preserve or use it. In other words, secure structures are required for lithium storage. Li-Based endohedral fullerene shows unique solid-state properties. The encapsulation of lithium atoms in fullerene helps to protect lithium atoms from external agents. Li-Based endohedral fullerenes have the potential to open the door to nanoscale lithium batteries. 119 For the development of endohedral metallofullerenes, larger fullerenes are generally required, as they possess large cages to accommodate lanthanide and transition metal atoms more smoothly. 118 Fullerene nanocages are useful for the storage of gases. Fullerene is under consideration for hydrogen storage. 120,121

Exohedral fullerenes carry more potential for applications as outer surfaces can be more easily modified or functionalized. The exohedral doping of metals into fullerenes strongly affects the electronic properties via shifting electrons from the metal to the fullerene nanocage. 122 The practical application of fullerenes can be achieved with tailor-made fullerene derivatives via chemical functionalization. As fullerene chemistry has matured, a wide range of functionalized fullerenes has been realized through simple synthetic routes. 123 The combination of hydrogen-bonding motifs and fullerenes has allowed the modulation of 1D, 2D, and 3D fullerene-based architectures. 124 The excellent electron affinities of fullerenes have shown great potential for eliminating reactive oxygen species. The presence of excess reactive oxygen species can cause biological dysfunction or other health issues. The surfaces of fullerenes have been functionalized via mussel-inspired chemistry and Michael addition reactions for the fabrication of C 60 –PDA–GSH. The developed C 60 –PDA–GSH nanoparticles demonstrated excellent potential for scavenging reactive oxygen species. 125

Amphiphiles have great importance in industrial processes and daily life applications. Amphiphilic molecules consist of hydrophilic and hydrophobic parts, and they perform functions in water via forming two- and three-dimensional assemblies. Recently, conical fullerene amphiphiles 126 have emerged as a new class of amphiphiles, in which a nonpolar apex is supplied by fullerenes and a hydrophilic part is achieved through functionalization. The selective functionalization of the fullerene on one side helps to achieve a supramolecule due to unique interfacial behavior. The unique supramolecular structure formed via the spontaneous assembly of one-sided selectively functionalized fullerenes through strong hydrophobic interactions between the fullerene apexes and polar functionalized portions is soluble in water. Conical fullerene amphiphiles are mechanically robust. Via upholding the structural integrity, conical fullerene amphiphiles can be readily aggregated with nanomaterials and biomolecules to form multicomponent agglomerates with controllable structural features. 127 Fullerenes, after suitable surface modification, have excellent potential for use in drug delivery, but there have only been limited explorations of their drug delivery applications. 128,129 Fullerene-based nano-vesicles have been developed for the delayed release of drugs. 130 Water-soluble proteins have great potential in the field of nanomedicine. The water-soluble cationic fullerene, tetra(piperazino)[60] fullerene epoxide (TPFE), has been used for the targeted delivery of DNA and siRNA specifically to the lungs. 131 For diseases in lungs or any other organ, efficient treatment requires the targeted delivery of active agents to a targeted place in the organ. The accumulation of micrometer-sized carriers in the lung makes lung-selective delivery difficult, as this may induce embolization and inflammation in the lungs. Size-controlled blood vessel carrier vehicles have been developed using tetra(piperazino)fullerene epoxide (TPFE). TPFE and siRNA agglutinate in the bloodstream with plasma proteins and, as a result, micrometer-sized particles are formed. These particles clog the lung capillaries and release siRNA into lungs cells; after siRNA delivery, they are immediately cleared from the lungs ( Fig. 14 ). 132

The supramolecular organization of fullerene (C 60 ) is a unique approach for producing shape-controlled moieties on the nano-, micro-, and macro-scale. Nano-, micro-, and macro-scale supramolecular assemblies can be controlled via manipulating the preparation conditions to achieve unique optoelectronic properties. 133 The development of well-ordered and organized 1D, 2D, and 3D fullerene assemblies is essential for achieving advanced optical and organic-based electronic devices. 134 Fullerene-based nanostructured materials with new dimensions are being developed from zero-dimensional fullerene and tuned to achieve the desired characteristics. 1D C 60 fullerene nanowires have received substantial attention over other crystalline forms due to their excellent features of potential quantum confinement effects, low dimensionality, and large surface areas. 135

Carbon nanomaterials are also used as supports for catalysts, and the main reasons to use them are their high surface areas and electrical conductivities. Carbon supports strongly influence the properties of metal nanoparticles. In fuel cells, the carbon support strongly affects the stability, electronic conductivity, mass transport properties, and electroactive surface area of the supported catalyst. 136 In fuel cells, the degradation of some catalysts, such as platinum-based examples, and carbon is correlated and reinforced as a result of both being present. Carbon support oxidation is catalyzed by platinum and the oxidation of carbon accelerates platinum-catalyst release. Overall, this results in a loss of catalytically active surface area. 137 Fullerenes are considered suitable support materials due to their excellent electrochemical activities and stability during electrochemical reactions. 138 Due to their high stability and good conductivity, fullerenes can replace conventional carbon as catalyst support materials. Fullerenes are also used for the development of efficient solar cells. 139

Apart from the applications mentioned above, fullerenes have a broader spectrum of applications where they can be used to improve outcomes considerably. Fullerenes have the potential to be used in the development of superconductors. 140 The strong covalent bonds in fullerenes make them useful nanomaterials for improving the mechanical properties of composites. 141 The combination of fullerenes with polymers can result in good flame-retardant and thermal properties. 142 Fullerenes and their derivatives are used for the development of advanced lubricants. They are used as modifiers for greases and individual solid lubricants. 138 Fullerenes have tremendous medicinal importance due to their anticancer, antioxidant, anti-bacterial, and anti-viral activities. 104

Fullerenes are vital members of the carbon-based nanomaterial family and they certainly possess exceptional properties. This discussion further emphasizes their importance for advanced applications. However, the discovery of other carbon-based nanomaterials has put fullerenes in the shade, and the pace of their exploration has been reduced. As fullerenes are highly symmetrical molecules with unique properties, they can act as performance boosters, but more attention is needed from researchers for their practical expansion. 110

Single-walled carbon nanotubes consist of a seamless one-atom-thick graphitic layer, in which carbon atoms are connected through strong covalent bonds. 146 Double-walled carbon nanotubes consist of two single-walled carbon nanotubes. One carbon nanotube is nested in another nanotube to construct a double-walled carbon nanotube. 147 In multi-walled carbon nanotubes, multiple sheets of single-layer carbon atom are rolled up. In other words, many single-walled carbon nanotubes are nested within each other. From different types of nanotubes, it can be concluded that the nanotubes may consist of one, tens, or hundreds of concentric carbon shells, and these shells are separated from each other with a distance of ∼0.34 nm. 148 Carbon nanotubes can be synthesized via chemical vapor deposition, 149 laser ablation, 150 arc-discharge, 143 and gas-phase catalytic growth. 151

Single-walled carbon nanotubes display a diameter of 0.4 to 2 nm. The inner wall distance between double-walled carbon nanotubes was found to be in the range of 0.33 to 0.42 nm. MWCNT diameters are usually in the range of 2–100 nm, and the inner wall distance is about 0.34 nm. 147,152 However, it is essential to note that the diameters and lengths of carbon nanotubes are not well defined, and they depend on the synthesis route and many other factors. The electrical conductivities of SWCNTs and MWCNTs are about 10 2 –10 6 S cm −1 and 10 3 –10 5 S cm −1 , respectively. SWCNTs and MWCNTs also display excellent thermal conductivities of ∼6000 W m −1 K −1 and ∼2000 W m −1 K −1 , respectively. CNTs remain stable in air at temperatures higher than 600 °C. 153 These properties indicate that CNTs have obvious advantages over graphite.

Single-walled carbon nanotubes can display metallic or semiconducting behavior. Whether carbon nanotubes show metallic or semiconducting behavior depends on the diameter and helicity of the graphitic rings. 154 The rolling of graphene sheets leads to three different types of CNTs: chiral, armchair, and zigzag ( Fig. 15 ). 155

Carbon nanotubes demonstrate some amazing characteristics that make them valuable nanomaterials for possible practical applications. Theoretical and experimental studies of carbon nanotubes have revealed their extraordinary tensile properties. J. R. Xiao et al. used an analytical molecular structural mechanics model to predict SWCNT tensile strengths of 94.5 (zigzag nanotubes) and 126.2 (armchair nanotubes) GPa. 156 In another study, the Young's modulus and average tensile strength of millimeter-long multi-walled carbon nanotubes were analyzed and found to be 34.65 GPa and 0.85 GPa, respectively. 157 Carbon nanotubes possess a high aspect ratio. Due to their high tensile strength, carbon nanotubes are used to enhance the mechanical properties of composites.

Carbon nanotubes have become an important industrial material and hundreds of tonnes are produced for applications. 158 Their high tensile strength and high aspect ratio have made carbon nanotubes an ideal reinforcing agent. 159 Carbon nanotubes are lightweight in nature and are used to produce lightweight and biodegradable nanocomposite foams. 160 The structural parameters of carbon nanotubes define whether they will be semiconducting or metallic in nature. This property of carbon nanotubes is considered to be effective for their use as a central element in the design of electronic devices such as rectifying diodes, 161 single-electron transistors, 162 and field-effect transistors. 163 The chemical stability, nano-size, high electrical conductivity, and amazing structural perfection of carbon nanotubes make them suitable for electron field emitter applications. 164 The unique set of mechanical and electrochemical properties make CNTs a valuable smart candidate for use in lithium-ion batteries. 165 CNTs have the full potential to be used as a binderless free-standing electrode for active lithium-ion storage. CNT-based anodes can have reversible lithium-ion capacities exceeding 1000 mA h g −1 , and this is a substantial improvement compared with conventional graphite anodes. In short, the following factors play a role in controlling and optimizing the performances of CNT-based composites: 166 (i) the volume fraction of carbon nanotubes; (ii) the CNT orientation; (iii) the CNT matrix adhesion; (iv) the CNT aspect ratio; and (iv) the composite homogeneity.

For some applications, a proper stable aqueous dispersion of CNTs at a high concentration is pivotal to allow the system to perform its function efficiently and effectively. 167 One of the major issues associated with carbon nanotubes is their poor dispersion in aqueous media due to their hydrophobic nature. Clusters of CNTs are formed due to van der Waals attraction, π–π stacking, and hydrophobicity. The CNT clusters, due to their strong interactions, hinder solubility or dispersion in water or even organic-solvent-based systems. 168 This challenging dispersion associated with CNTs has limited their use for promising applications, such as in biomedical devices, drug delivery, cell biology, and drug delivery. 167 Carbon nanotube applications and inherent characteristics can be further tuned via suitable functionalization. The functionalization of carbon nanotubes helps scientists to manipulate the properties of carbon nanotubes and, without functionalization, some properties are not attainable. 169 The functionalization of nanotubes can be divided into two main categories: covalent functionalization and non-covalent functionalization.

The heating of CNTs under strongly acidic and oxidative conditions results in the formation of oxygen-containing functionalities. These functional groups, such as carboxylic acid, react further with other functional groups, such as amines or alcohols, to produce amide or ester linkages on the carbon nanotubes. 172 One of the main issues preventing the utilization of CNTs for biomedical applications is their toxicity. The cytotoxicity of pristine carbon nanotubes can be reduced via introducing carbonyl, –COOH, and –OH functional groups. Apart from functionalization through oxidized CNTs, the direct functionalization of CNTs is also possible. However, direct functionalization requires more reactive species to directly react with the CNTs, such as free radicals. Addition reactions to CNTs can cause a transformation from sp 2 hybridization to sp 3 hybridization at the point of addition. At the point where functionalization has taken place, the local bond geometry is changed from trigonal planar to tetrahedral geometry. Some addition reactions to the sidewalls of CNTs are shown in Fig. 16 . 155

It is important to discuss how the covalent functionalization of carbon nanotubes comes at the price of the degradation of the carbon sp 2 network. This substantially affects the electronic, thermal, and optoelectronic properties of the carbon nanotubes. 169 Efforts are being made to introduce a new method of covalent functionalization that can keep the π network of CNTs intact. Antonio Setaro et al. introduced a new [2+1] cycloaddition reaction for the non-destructive, covalent, gram-scale functionalization of single-walled carbon nanotubes. The reaction rebuilds the extended π-network, and the carbon nanotubes retained their outstanding quantum optoelectronic properties ( Fig. 17 ). 173

Polymers are frequently combined with CNTs to enhance their dispersion capabilities. Polymers interact with CNTs through CH–π and π–π interactions. 174 Hexanes and cycloalkanes are poor CNT solvents but the good solubility or dispersion of CNTs in these solvents is required for surface coating applications. Poly(dimethylsiloxane) (PDMS) macromer-grafted polymers have been prepared using PDMS macromers and pyrene-containing monomers that strongly adsorb on CNTs, thus improved the solubility of CNTs in chloroform and hexane. 176 The use of head–tail surfactants is another attractive way to achieve a fine dispersion of CNTs in an aqueous medium. In head–tail surfactants, the tail is hydrophobic and interacts with the CNT sidewalls, and the hydrophilic head groups interact with the aqueous environment to provide a fine dispersion. 177

For electrical applications, non-covalently functionalized CNTs are more preferred because the electrical properties of the CNTs are not compromised. CNTs have been non-covalently functionalized with a variety of biomolecules for the fabrication of electrochemical biosensors. 175 Non-covalently functionalized SWCNTs are used for energy applications. Single-walled carbon nanotubes (SWCNTs) have been non-covalently functionalized with 3d transition metal( II ) phthalocyanines, lowering the potential of the oxygen evolution reaction by approximately 120 mV compared with unmodified SWCNTs. 178 The toxicity of pristine CNTs toward living organisms can be lessened via using surfactant-functionalized CNTs. 170 However, in some cases, during polymer non-covalent functionalization, the polymer may wrap CNT bundles and make it difficult to separate the CNTs from each other. Polymers can develop into insulating wrapping that affects the CNT conductivity.

In the literature, several graphene-related materials have been reported, such as graphene oxide and reduced graphene oxide. 187 Among graphenoids, graphene oxide is a more reported and explored graphene-related material as a precursor for chemically modified graphene. The synthetic route to graphene oxide is straightforward, and it is synthesized from inexpensive graphite powder that is readily available. 188 Graphene oxide has many oxygen-containing functional groups, such as epoxy, hydroxyl, carboxyl, and carbonyl groups. The basal plane of graphene oxide is generally decorated with epoxide and hydroxyl groups, whereas the edges presumably contain carboxyl- and carbonyl-based functional groups. 189 The presence of active functional groups in graphene oxide allows its further functionalization with different polymers, small organic compounds, or other nanomaterials to realize several applications. 190

Graphene oxide, due to its oxygen functionality, is insulating in nature and displays poor electrochemical performance. The presence of oxygen functionalities in graphene oxide breaks the conjugated structure and localizes the π-electron network, resulting in poor carrier mobility and carrier concentration. 196 Its electrochemical performance is improved substantially after removing the oxygen-containing functional groups. 197 These functional groups can be removed or reduced via thermal, electrochemical, and chemical means. The product obtained after removing or reducing oxygen moieties is called reduced graphene oxide. The properties of reduced graphene oxide depend upon the effective removal of oxygen moieties from graphene oxide. The process used to remove oxygen-containing functionalities from graphene oxide will determine the extent to which reduced the properties of graphene oxide resemble pristine graphene. 198

Reduced graphene oxide is extensively used to improve the performances of various electrochemical devices. 199 It is essential to mention that even after reducing graphene oxide, some residual sp 3 carbon bonded to oxygen still exists, which somehow disturbs the movement of charge through the delocalized electronic cloud of the sp 2 carbon network. 200 Apart from this, the electrochemical activity of reduced graphene oxide is substantially high enough to manufacture electrochemical devices with improved performances. Recently, the demand for super-performance electrochemical devices has increased to overcome modern challenges relating to electronics and energy-storage devices. 201 Graphene-based materials are considered to be excellent electrode materials, and they can be proved to be revolutionary for use in energy-storage devices such as supercapacitors (SC) and batteries. Graphene-based electrodes improve the performances of existing batteries (lithium-ion batteries) and they are considered useful for developing next-generation batteries such as sodium-ion batteries, lithium–O 2 batteries, and lithium–sulfur batteries ( Fig. 18 ). Being flat in nature, each carbon atom of graphene is available, and ions can easily access the surface due to low diffusion resistance, which provides high electrochemical activity. 202

Graphene and its derivatives are extensively used for the development of electrochemical sensors. 203 The surfaces of bare electrodes are usually not able to sense analytes at trace levels and they cannot differentiate between analytes that have close electrooxidation properties due to their poor surface kinetics. The addition of graphene layers to the surfaces of electrodes can substantially improve the electrocatalytic activity and surface sensitivity towards analytes. 204 Graphene has definite advantages over other materials that are used as electrode materials for sensor applications. Graphene has a substantially high surface-to-volume ratio and atomic thickness, making it extremely sensitive to any changes in its local environment. This is an essential factor in developing advanced sensing tools, as all the carbon atoms are available to interact with target species.

Consequently, graphene exhibits higher sensitivity than its counterparts such as CNTs and silicon nanowires. 205 Graphene has two main advantages over CNTs for the development of electrochemical sensors. First, graphene is mostly produced from graphite, which is a cost-effective route, and second, graphene does not contain metallic impurities like CNTs can. Graphene offers many other advantages when developing sensors and biosensors, such as biocompatibility and π–π stacking interactions with biomolecules. 206 Graphene-based materials are ideal for the construction of nanostructured sensors and biosensors.

The mechanical properties of graphene are used to fabricate highly desired stretchable and flexible sensors. 207 Graphene can be utilized to develop transparent electrodes with excellent optical transmittance. It displays good piezoresistive sensitivity. Researchers are making efforts to replace conventional brittle indium tin oxide (ITO) electrodes with flexible graphene electrodes in optoelectronic devices such as liquid-crystal displays and organic light-emitting diodes. 208 For human–machine interfaces, transparent and flexible tactile sensors with high sensitivity have become essential. Graphene film (GF) and PET have been applied to develop transparent tactile sensors that exhibit outstanding cycling stability, fast response times, and excellent sensitivity ( Fig. 19 ). 209 Similarly, graphene is applied for the fabrication of pressure sensors. 210 Overall, graphene is an excellent material for developing transparent and flexible devices. 211,212

The use of graphene-based materials is an effective way to deal with a broad spectrum of pollutants. 213 There are many ways to deal with environmental pollution; among these, adsorption is an effective and cost-effective method. 214,215 Graphene-based adsorbents are found to be useful in the removal of organic, 216 inorganic, and gaseous contaminants. Graphene-based materials have some obvious advantages over CNT-based adsorbents. For example, graphene sheets offer two basal planes for contaminant adsorption, enhancing their effectiveness as an adsorbent. 192 GO contains several oxygen functional groups that impart hydrophilic features. Due to appropriate hydrophilicity, GO-based adsorbents can efficiently operate in water to remove contaminants. Moreover, graphene-oxide-based materials can be functionalized further through reactive moieties with various organic molecules to enhance their adsorption capacities. 217

In short, extensive research must continue in order to develop graphene-based materials with high performance and bring them to the market. Massive focus on graphene research is also justified due to the extraordinary features described in extensive theoretical and experimental research works.

Nanodiamonds possess a core–shell-like structure and display rich surface chemistry, and numerous functional groups are present on their surface. Several functional groups, such as amide, aldehyde, ketone, carboxylic acid, alkene, hydroperoxide, nitroso, carbonate ester, and alcohol groups, are present on nanodiamond surfaces, assisting in their further functionalization for desired applications ( Fig. 20 ). 226

Furthermore, nanodiamond surfaces can be homogenized with a single type of functional group according to the application requirements. 227 The use of nanodiamond particles as a reinforcing material in polymer composites has attracted great attention for improving the performance of polymer composite materials. The superior mechanical properties and rich surface chemistry of nanodiamonds have made them a superior material for tuning and reinforcing polymer composites. Nanodiamonds might operate via changing the interphase properties and forming a robust covalent interface with the matrix. 228 Nanodiamond (ND)-reinforced polymer composites have shown superior thermal stabilities, mechanical properties, and thermal conductivities. Nanodiamonds have shown great potential for energy storage applications. 229 Nanodiamonds and their composites are also used in sensor fabrication, environmental remediation, and wastewater treatment. 230,231 Their stable fluorescence and long fluorescence lifetimes have made nanodiamonds useful for imaging and cancer treatment. For biomedical applications, the rational engineering of nanodiamond particle surfaces has played a crucial role in the carrying of bioactive substances, target ligands, and nucleic acids, resisting their aggregation. 232,233 Nanodiamonds have a great future in nanotechnology due to their amazing surface chemistry and unique characteristics.

Carbon quantum dots can be synthesized through several chemical routes. 241–245 Some methodologies for synthesizing carbon dots are described in Fig. 21 . 246–248 Carbon itself is a black material and displays low solubility in water. In contrast, carbon quantum dots are attractive due to their excellent solubility in water. They contain a plethora of oxygen-containing functional groups on their surface, such as carboxylic acids. These functional moieties allow for further functionalization with biological, inorganic, polymeric, and organic species.

Carbon quantum dots are also called carbon nano-lights due to their strong luminescence. 248 In particular, carbon quantum dots offer enhanced chemiluminescence, 249,250 fluorescent emission, 251 two-photon luminescence under near-infrared pulsed-laser excitation, 252 and tunable excitation-dependent fluorescence. 253 The luminescence characteristics of carbon quantum dots have been used to develop highly sensitive and selective sensors. In most cases, a simple principle is involved in sensing with luminescent carbon quantum dots: their photoluminescence intensity changes upon the addition of an analyte. 254 Based on this principle, several efficient sensors have been developed using carbon quantum dots. 255–257 They can be used as sensitive and selective tools for sensing explosives such as TNT. Recognition molecules on the surfaces of carbon quantum dots can help to sense targeted analytes. Amino-group-functionalized carbon quantum dot fluorescence is quenched in the presence of TNT through a photo-induced electron-transfer effect between TNT and primary amino groups. This quenching phenomenon can help to sense the target analyte ( Fig. 22 ). 258 Chiral carbon quantum dots (cCQDs) can exhibit an enantioselective response. The PL responses of cCQDs were evaluated toward 17 amino acids and it was found that the PL intensity of the cCQDs was only substantially enhanced in the presence of L -Lys ( Fig. 22 ). 254

Carbon quantum dots have received significant interest in the fields of biological imaging and nanomedicine ( Fig. 23 ). 239 Direct images of RNA and DNA are essential for understanding cell anatomy. Due to the limitations of current imaging probes, tracking the dynamics of these biological macromolecules is not an easy job. Recently, membrane-penetrating carbon quantum dots have been developed for the imaging of nucleic acids in live organisms. 259 It is important to note that most of the carbon quantum dots utilized to attain cell imaging under UV excitation emit blue radiation. Some biological tissue also emits blue light, specifically that involving carbohydrates, and this interferes with cell imaging carried out with blue-emitting CQDs. This seriously hinders their potential in the field of biomedical imaging. Due to this reason, researchers are focusing on tuning CQDs in a way that their emission peak is red-shifted to avoid interference. 260 Carbon quantum dots with yellow and green fluorescence have been reported for bioimaging purposes. 261,262 The suitable doping of carbon quantum dots can red-shift the emission to enhance the bioimaging effectiveness. 263 Doped carbon quantum dots are capable of biological imaging and display advanced capabilities for scavenging reactive oxygen species. 264

Carbon quantum dots demonstrate photo-induced electron transfer properties 265 that make them valuable for photocatalytic, light-energy conversion, and other related applications. 266 Carbon quantum dots enhance the activities of other photocatalysts to which they are attached. Carbon quantum dots, along with photocatalysts, provide better charge separation and suppress the regeneration of photogenerated electron–hole pairs. Moreover, the proper implantation of carbon quantum dots into photocatalysts can broaden the photo-absorption region. Implanted carbon quantum dots form micro-regional heterostructures that facilitate photo-electron transport. 267 The implantation of carbon quantum dots into g-C 3 N 4 can substantially enhance charge transfer and separation efficiencies, prevent photoexcited carrier recombination, narrow the bandgap, and red shift the absorption edge. 268 The intrinsic catalytic activity of polymeric carbon nitride is improved as a result of the nano-frame heterojunctions formed with the help of CQDs. 269

Carbon quantum dots offer many advantages over conventional semiconductor-based QDs and, thus, they have attracted considerable researcher attention. 244 Due to their remarkable features, they have shown importance in recent years in the fields of light-emitting diodes, nanomedicine, solar cells, sensors, catalysis, and bioimaging. 236

The production of carbon nanohorns has some obvious advantages over carbon nanotubes, such as the ability for toxic-metal-catalyst-free synthesis and large-scale production at room temperature. Carbon nanotube synthesis involves metal particles, and harsh conditions, such as the use of strong acids, are required to remove metallic catalysts. This process introduces many defects into CNT structures and may cause a loss of carbon material. 270 Carbon nanohorns possess a wide diameter compared to CNTs. CNHs possess good absorption capabilities and their interiors are also available after partial oxidation, which provides direct access to their internal parts. Heat treatment under acidic or oxidative conditions facilitates the facile introduction of holes into carbon nanohorns. Holes in graphene sheets of single-walled carbon nanohorns can be produced with O 2 gas at high temperatures. A large quantity of material can be stored inside CNH tubes. 274 The surface area of CNHs is substantially enhanced upon opening the horns to make their interiors accessible. 275 Carbon nanohorns have great potential for energy storage, 275 electrochemiluminescence, 276 adsorption, 277 catalyst support, 278 electrochemical sensing, 279 and drug delivery system 273 uses. CNHs as catalyst supports can provide a homogeneous dispersion of Pt nanoparticles ( Fig. 25 ). The current density of Pt supported on single-walled carbon nanohorns is double compared to a fuel cell made from Pt supported on carbon black. 280 Thus, carbon nanohorns provide a better uniform dispersion that facilitates a high surface area and better catalyst performance.

5.2. Nanoporous materials

In nanoporous materials, the size distributions, volumes, and shapes of the pores directly affect the performances of porous materials for particular applications. It has become a hot area of research to develop materials with precisely controlled pores and arrangements. Recent research has focused more on the precise control of the shapes, sizes, and volumes of pores to produce nanoporous materials with high performance. Several state-of-the-art reviews are present in the literature that focus explicitly on the synthesis, properties, advances, and applications of nanoporous materials. 85,287–289 Based on the materials used, nanoporous materials can be divided into three main groups: inorganic nanoporous materials; carbonaceous nanoporous materials; and organic polymeric nanoporous materials.

Inorganic nanoporous materials include porous silicas, clays, porous metal oxides, and zeolites. The generation of pores in the material can introduce striking features into the material that are absent in non-porous materials. Nanoporous materials offer rich surface compositions with versatile characteristics. Nanoporous materials exhibit high surface-to-volume ratios. Their outstanding features and nanoporous framework structures have made these materials valuable in the fields of environmental remediation, adsorption, catalysis, sensing, energy conversion, purification, and medicine. 284,290

Porous silica is a crucial member of the inorganic nanoporous family. Over the decades, it has generated significant research interest for use in fuel cells, chemical engineering, ceramics, and biomedicine. It is essential to note that specific morphologies and pore size diameters are required for each application, and these can be achieved via tuning during the synthesis process. Nanoporous silica offers two functional surfaces: one is the cylindrical pore surfaces, and the second is the exterior surfaces of the nanoporous silica particles. The surfaces of nanoporous silica can be easily functionalized for the desired applications. The nanoporous silica surface is heavily covered with many silanol groups that act as reactive sites for functionalization ( Fig. 26 ). 291,292 For biomedical applications, mesoporous silica has emerged as a new generation of inorganic platform materials compared to other integrated nanostructured materials. Several factors make it a unique material for biomedical applications: 293,294 (a) its ordered porous structure; (b) its tunable particle size; (c) its large pore volume and surface area; (d) its biocompatibility; (e) its biodegradation, biodistribution, and excretion properties; and (f) its two functional surfaces. For instance, ordered MCM-48 nanoporous silica was used for the delivery of the poorly soluble drug indomethacin. It has been found that surface modification can control drug release. 295 Mesoporous silica-based materials have emerged as excellent materials for use in sustained drug delivery systems (SDDSs), immediate drug delivery systems (IDDSs), targeted drug delivery systems (TDDSs), and stimuli-responsive controlled drug delivery systems (CDDSs). The drug release rate from mesoporous silica can also be controlled via introducing appropriate polymers or functional groups, such as CN, SH, NH 2 , and Cl. Researchers are currently focusing on developing MSN-based (MSN = mesoporous silica nanoparticle) multifunctional drug delivery systems that can release antitumor drugs on demand in a targeted fashion via minimizing the premature release of the drug ( Fig. 27 ). 296

Hierarchically nanoporous zeolites are a vital member of the nanoporous material family. They are crystalline aluminosilicate minerals whose structures comprise uniform, regular arrays of nanopores with molecular dimensions. The microporous structures of zeolites contain pores that are usually below 1 nm in diameter. In zeolites, the micropores are uniform in shape and size, and these pores can effectively discriminate between molecules based on shape and size. 297 Currently, based on crystallography, more than 200 zeolites have been classified. 298 Zeolites have been proved to be useful materials in the field of host–guest chemistry. In solid catalysis, about 40% of the entire solid catalyst field is taken up by zeolites in chemical industry. The excellent catalysis success of zeolites is based on their framework stability, shape-selective porosity, solid acidity, and ion-exchange capacity. Oxygen tetrahedrally coordinates with the Al atoms in most zeolite crystalline silicate frameworks, resulting in charge mismatch between the oxide framework and Al. Extra-framework Na + ions compensate for this charge mismatch. The Na + ions are exchangeable for other cations such as H + and K + . 298 The zeolite crystalline networks are remarkable in that they provide high mechanical and hydrothermal stabilities. The most crucial task facing the zeolite community is to find new structures with desired functions and apply them more effectively for different applications.

Apart from these inorganic porous materials, several other metal- and metal-oxide-based nanoporous materials have been introduced that are more prominent for use in electrode material, catalyst, photodegradation, energy storage, and energy conversion applications. 299–302 Nanoporous metal-based materials are famous due to the nanosized crystalline walls, interconnected porous networks, and numerous surface metal sites that provide them with unique physical/chemical properties compared with their bulk counterparts and other nanostructured materials. 303 For example, nanoporous WO 3 films were developed via tuning the anodization conditions for photoelectrochemical water oxidation. It has been observed that the morphology of the film strongly affected the photoelectrochemical performance. 304 Nanoporous alumina is also a unique material in the inorganic nanoporous family due to several aspects. Nanoporous alumina can be prepared in a controlled fashion with any size and shape in polyprotic aqueous media via the anodic oxidation of the aluminum surface. The parallel arrangement of pores on alumina can easily be controlled from 5 nm to 300 nm, and alumina is stable in the range of 1000 °C. The anodizing time plays a significant role in controlling the pore length. Nanoporous alumina membranes offer various unique properties, such as pores of variable widths/lengths, temperature stability, and optical transparency. Nanoporous alumina pores can be filled with magnetically and optically active elements to produce the desired applications at the nanoscale level. Photoluminescent alumina membranes can be produced via introducing cadmium sulfide, gallium nitride, and siloxenes inside nanoporous alumina using appropriate precursors. 305 Porous alumina also acts as an efficient support and template for the designing of other nanomaterials. Palladium nanowires, 306 high aspect ratio cobalt nanowires, 307 and highly aligned Cu nanowires 308 were developed using porous alumina as a template. Ni–Pd as a catalyst was supported on porous alumina for hydrogenation and oxidation reactions. 309 Nanoporous anodic alumina is also considered to be an efficient material for the development of biosensors due to the ease of fabrication, tunable properties, optical/electrochemical properties, and excellent stability in aqueous environments. 310

Nanoporous carbon-based materials are a hot topic in the field of materials chemistry. Nanoporous carbon materials have become ubiquitous choices in the environmental, energy, catalysis, and sensing fields due to their unique morphologies, large pore volumes, controlled porous structures, mechanical, thermal, and chemical stabilities, and high specific surface areas ( Fig. 28A ). 311 Nanoporous materials are found to be useful in the treatment of water. The separation of spilled oil and organic pollutants from water has emerged as a significant challenge. 312–314 The design of materials that can allow the efficient separation of organic, dye, and metal contaminants from water has become a leading environmental research area. 315,316 Nanoporous carbon can be derived from different natural and synthetic sources. 317–319 Nanoporous carbon foam can be derived from natural sources, such as flour, pectin, and agar, via table-salt-assisted pyrolysis. The agar-derived nanoporous carbon foam showed high absorption capacities, a maximum of 202 times its own weight, for oil and organic solvents. Air filtration paper developed from carbon nanoporous materials and non-woven fabrics has shown a filtration efficiency of greater than 99% ( Fig. 28B ). 320 Nanoporous carbon can also be produced from other porous frameworks, such as metal–organic frameworks. MOF- and COF-based materials are promising precursors for nanoporous carbon-based materials. The direct carbonization of amino-functionalized aluminum terephthalate metal–organic frameworks has produced nitrogen-doped nanoporous carbon that shows an adequate removal capacity of 98.5% for methyl orange under the optimum conditions. 321 Fe 3 O 4 /nanoporous carbon was also produced with Fe salts as a magnetic precursor and MOF-5 as a carbon precursor for removing the organic dye methylene blue (MB) from aqueous solutions. 322 The mesoporous carbon removal efficiency could be further enhanced via modifying or functionalizing the surface with various materials. Unmodified mesoporous carbon has shown a mercury removal efficiency of 54.5%. This efficiency can be substantially improved to 81.6% and 94% upon modification with the anionic surfactant sodium dodecyl sulfate and cationic surfactant cetyltrimethyl ammonium bromide (CTAB), respectively. 323

Ordered nanoporous carbon, CNTs, and fullerenes are extensively applied for energy and environmental applications. The complicated synthesis routes required for fullerenes and CNTs have slowed down the full exploitation of their potential for highly demanding applications. In comparison, the synthesis of highly ordered nanoporous carbon is facile, and the properties of ordered nanoporous carbon are also appealing for energy and environmental applications. 311 CO 2 is a greenhouse gas, and its sustainable conversion into value-added products has become the subject of extensive research. A nitrogen-doped nanoporous-carbon/carbon-nanotube composite membrane is a high-performance gas-diffusion electrode applied for the electrocatalytic conversion of CO 2 into formate. A faradaic efficiency of 81% was found for the production of formate. 324 Nanoporous carbon materials modified with the non-precious elements P, S, N, and B have emerged as efficient electrode materials for use in the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), batteries, and fuel cells. 311,325–327

Nanoporous polymers, including nanoporous coordination polymers and crystalline nanoporous polymers, have emerged as impressive nanoporous materials. 328 Nanoporous polymers have many applications, and these materials are extensively being evaluated for gas separation and gas storage. The great interest in these applications arises from the presence of pores providing an exceptionally high Brunauer–Emmett–Teller (BET) surface area. Recently, new classes of metal organic framework and covalent organic framework porous materials have been reported that have shown exceptionally high and unprecedented surface areas. For instance, in 2010, a MOF was reported with a surface area of 6143 m 2 g −1 ; 329 in 2012, a MOF was reported with a surface area greater than 7000 m 2 g −1 ; 330 and in 2018, a MOF (DUT-60) was reported with a record surface area of 7836 m 2 g −1 . 331 Mesoporous DUT-60 has also shown a high free volume of 90.3% with a density of 0.187 g cm −3 . 331

Due to their exceptionally high surface areas and porous networks, these MOFs and COFs are ideal for gas storage. Air separation and post-combustion CO 2 capture have become integral parts of mainstream industries related to the energy sector in order to avoid substantial economic penalties. Due to the inefficiencies of available technology and the critical importance of this area, earnest efforts are being made to design gas-selective porous materials for the selective adsorption of desired gases. Nanoporous MOF- and COF-based materials can significantly capture CO 2 and help reach zero or minimum CO 2 emission levels. For instance, nanoporous fluorinated metal–organic frameworks have shown the selective adsorption of CO 2 over H 2 and CH 4 . 332 Hasmukh A. Patel et al. developed N 2 -phobic nanoporous covalent organic polymers for the selective adsorption of CO 2 over N 2 . The azo groups in the framework rejected N 2 , leading to CO 2 selectivity. 333 Nanoporous polymers that are superhydrophobic in nature can also be used for volatile organic compounds and organic contaminants. 334 Nanoporous polymers, due to the presence of a porous network, have been considered as highly suitable materials for catalyst supports. Furthermore, organocatalytic functional groups can be introduced pre-synthetically and post-synthetically into solid catalysts. 335

Nanoporous polymeric materials are amazingly heading towards being extremely lightweight with exceptionally high surface areas. These high surface areas and the fine-tuning of the nanopores has made these nanoporous materials, specifically MOFs and zeolites, ideal support materials for encapsulating ultrasmall metal nanoparticles inside void spaces to produce nanocatalysts with exceptionally high efficiencies. 336 In the coming years, more exponential growth of nanoporous materials is expected in the energy, targeted drug delivery, catalysis, and water treatment fields.

5.3. Ultrathin two-dimensional nanomaterials beyond graphene

However, from a material synthesis standpoint, a graphite-like layered form of Si does not exist in nature and there is no conventional exfoliation process that can generate 2D silicene, although single-walled 351 and multi-walled 352 silicon nanotubes and even monolayers of silicon have been synthesized via exfoliation methods. 353 Forming honeycomb Si nanostructures on substrates like Ag(001) and Ag(110) via molecular beam deposition, so-called “epitaxial growth”, was then proposed as a method for the architectural design of silicene sheets. 354–356 The successful synthesis of a silicene monolayer was first achieved on Ag(111) and ZrB 2 (0001) substrates in 2012; 357,358 later, various demonstrations were made using Ir(111), ZrB 2 (001), ZrC(111), and MoS 2 surfaces as the silicene growth substrates. 359–361 Despite various extensive studies to date involving the “epitaxial growth” of silicene on different substrates and investigations of the electronic properties, 357,362–364 the limited nanometer size, difficulties relating to substrate removal, and air stability issues have substantially impeded the practical applications of silicene. Bearing in mind all these known difficulties, Akinwande and co-workers recently reported a growth–transfer–fabrication process for novel silicene-based field-effect transistor development that involved silicene-encapsulated delamination with native electrodes. 365 An etch-back approach was used to define source/drain contacts in Ag film. Without causing any damage to the silicene, a novel potassium-iodide-based iodine-containing solution was used to etch Ag, avoiding rapid oxidation, unlike other commonly used Ag etchants. The results demonstrated that this was the first proof-of-concept study confirming the Dirac-like ambipolar charge transport predictions made about silicene devices. Comparative studies with a graphene system, the low residual carrier density, and the high gate modulation suggested the opening of a small bandgap in the experimental devices, proving that silicene can be considered a viable 2D nanomaterial beyond graphene.

Nonetheless, the synthesis of silicene on a large-scale is greatly limited, as “epitaxial growth” is the only promising method for obtaining high-quality silicene, and this presents an enduring challenge in relation to silicene research and development. Xu and co-workers recently introduced liquid oxidation and the exfoliation of CaSi 2 as a means for the first scalable preparation of high-quality silicene nanosheets. 366 This new synthetic strategy successfully induced the exfoliation of stacked silicene layers via the mild oxidation of the (Si 2 n ) 2 n layers in CaSi 2 into neutral Si 2 n layers without damage to the pristine silicene structure ( Fig. 29 ). The selective oxidation of pristine CaSi 2 into free-standing silicene sheets without any damage to the original Si framework was carried out via exfoliation in the presence of I 2 in acetonitrile solvent. Furthermore, the obtained silicene sheets yielded ultrathin monolayers or layers with few-layer thickness and exhibited excellent crystallinity. This 2D silicene nanosheet material was extensively explored as a novel anode, which was unlike previously developed silicon-based anodes for lithium-ion batteries. It displayed a theoretical capacity of 721 mA h g −1 at 0.1 A g −1 and superior cycling stability of 1800 cycles. Overall, during the last decade, silicene has been widely accepted as an ideal 2D material with many fascinating properties, suggesting great promise for a future beyond graphene.

Like other 2D materials, MXenes exhibit crystal geometry with a hexagonal close-packed structure based on the equivalent MAX-phase precursor, and the close-packed structure is formed from M atoms with X atoms occupying octahedral sites. 371 According to the formula, there are three representative structures of MXenes: M 2 XT x , M 3 X2T x , and M 4 X3T x . In these combinations, X atoms are formed with n layers, whereas M atoms have n + 1 layers ( Fig. 30 ). 372 Apart from graphene, MXenes are considered the most dynamic developing material, and they have incredible innovation potential amongst typical 2D nanomaterials because of their remarkable properties, such as hydrophilicity, conductivity, considerable adsorption abilities, and catalytic activity. These vital properties of MXenes suggest their use for various potential applications, including in the photocatalysis, electrocatalysis, 373,374 energy, 375 membrane-based separation, 376,377 and biological therapy 378 fields. In this section, we focus on describing new developments relating to MXenes that are utilized for electrocatalytic and energy storage applications, competing as alternatives to graphene materials.

Interestingly, due to the presence of abundant terminal groups, mainly –O, –OH, and –F, and their modifying nature, MXenes can exhibit outstanding hydrophilic properties and high conductivity and charge carrier mobility, making them a very attractive material for various electrocatalytic applications, such as the hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, nitrogen reduction reaction, and CO 2 reduction reaction. To further increase their electrocatalytic activities, recent works involving MXenes have included incorporation with CNTs, 379 g-C 3 N 4 , 380 FeNi-LDH, 381 NiFeCo-LDH, 382 and metal–organic frameworks. 383

Cho and co-workers designed and developed MXene–TiO 2 2D nanosheets via the surface oxidation of MXene with defect-free control. These MXene–TiO 2 2D nanosheets were successfully implemented in nano-floating-gate transistor memory (NFGTM) providing a floating gate ( i.e. , multilayer MXene) and tunneling dielectric ( i.e. , the TiO 2 layer). A process of oxidation in water further represented a cost-effective and environmentally benign method, as depicted in Fig. 31 . The MXene NFGTM with an optimal oxidation process displayed exceptional nonvolatile memory features, having a great memory window, high programming/erasing current ratio, long term retention, and high durability. 384

There have been some exciting reports on 2D materials from the pnictogen family, particularly phosphorene. Recently, more attention has also been given to the remaining group 15 elements, 390 with the novel 2D materials arsenene, antimonene, and bismuthene being obtained from the key elements arsenic, antimony, and bismuth, respectively. It is reported that 2D monolayers of group 15 elements, including phosphorene allotropes, have five distinct honeycomb (α, β, γ, δ, and ε) and four distinct non-honeycomb (ζ, η, θ, and ι) structures, as depicted in Fig. 32 . Dissimilar crystal orientations were found for single-layered As, Sb, and Bi. Zeng and co-workers also reported comprehensive density functional theory (DFT) computations that proved the energetic stability and broad-range application of these materials in 2D semiconductors. 391 Previously, following theoretical predictions, Wu and co-workers successfully demonstrated that α-phosphorene showed lowest energy configurations in both honeycomb and non-honeycomb nanosheets. 392 In contrast, Zeng and co-workers proved that the buckled forms of 2D sheets of As, Sb, and Bi allotropes are the most stable structures, particularly their β phases. 391

Among monolayer group 15 family materials, 2D sheets of arsenic (As) and antimony (Sb) have gained considerable attention from researchers. 393,394 Studies have shown that As and Sb exhibit better stability than black phosphorus; they are highly stable at room temperature and less reactive to air, likely inhibiting the oxidization process. 395–398 Nevertheless, it has been demonstrated that the oxidation process is perhaps favorable for fine-tuning the electronic properties; increases in the indirect band gaps ranging from 0 to a maximum of 2.49 eV are found in free-standing arsenene and antimonene semiconductors. 399–403 Simultaneously, arsenene and antimonene can also be transformed into semiconductors with direct band gaps. These two 2D nanosheets can be used to design mechanical sensors, moving beyond common electronic and optoelectronic applications. These two extraordinary 2D nanosheets have been studied for their structural–property relationships via first-principles methods. 403–405

Continuing the characterization and structural property studies of arsenene carried out by Kamal 404 et al. and Zhang 403 et al. , Anurag Srivastava and co-workers analyzed applications of arsenene to explore the possibility of improving sensor devices that can be utilized to detect ammonia (NH 3 ) and nitrogen dioxide (NO 2 ) molecules. 406,407 They investigated the affinities of NH 3 and NO 2 molecules for pristine arsenene sheets, examining the binding energies, bonding distances, density distributions, and current–voltage features. The results showed that arsenene 2D sheets are highly durable, with significant electronic charge transfer. They also considered germanium-doped arsenene and characterized the 2D lattice based on molecular affinity relationships with respect to the dopant.

However, the incorporation of any dopants into 2D nanomaterials not only results in experimental difficulty but it also lowers the stability of 2D materials. 408 Recently, Dameng Liu and co-workers reported the electronic structures, focusing on band structures, band offsets, and intrinsic defect properties, of few-layer arsenic and antimony. 409 The spontaneous oxide passivation layer that is formed naturally on pristine antimonene provides excellent stability. 410 Very recently, Stefan Wolff and co-workers conducted DFT calculations on various single or few-layer antimony oxide structures to describe the stoichiometry and bonding type. Interestingly, the samples exhibited various structural stabilities and electronic properties with a wide range of direct and indirect band gaps. Showing band gaps between 2.0 and 4.9 eV, these 2D layers of antimonene exhibited the potential to be used as insulators or semiconductors. 411 The same group also analyzed Raman spectra and discussed identifying the predicted antimonene oxide structures experimentally. The enduring task of exploring the utility of antimonene has boosted recent research interest in 2D nanomaterials due to the broad range of potential applications, such as their use in electrochemical sensors, 412,413 stable organic solar cells, 414 and supercapacitors 415 to name a few.

The 2D MOF nanosheets are also evaluated for the development of high-performance power-storage devices. For example, Li et al. 427 recently reported two novel Mn-2D MOFs and Ni-2D MOFs as anode materials for rechargeable lithium batteries. The Mn-based ultrathin metal–organic-framework nanosheets, due to thinner nanosheets, a higher specific surface area, and smaller metal ion radius, had structural advantages over Ni-based ultrathin metal–organic-framework nanosheets. Due to these features, the Mn-based ultrathin metal–organic-framework nanosheets displayed a high reversible capacity of 1187 mA h g −1 at 100 mA g −1 for 100 cycles and a rate capability of 701 mA h g −1 even at 2 A g −1 .

The expensive metal oxides utilized in the catalytic process can be replaced in due course by 2D-MOF-based nanosheets with exposed metal sites that impart an adjustable pore structure, ultrathin thickness, a high surface-to-volume atom ratio, and high design flexibility. As a result, 2D-MOFs have extensively been explored for various electrocatalytic applications, including the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and carbon dioxide reduction reaction (CO 2 RR). For example, Marinescu et al. 428 combined cobalt dithiolene species with benzenehexathiol (BHT) and yielded 2D-MOFs capable of acting as electrocatalysts for the HER in water ( Fig. 34 ). In the presence of 2D-MOF sheets, a high current density of 41 mA cm −2 , at −0.8 V vs. SHE and a pH value of 1.3, is observed. Similarly, Feng et al. 429 also developed single-layer Ni-based 2D-MOF sheets that are highly effective for electrocatalytic hydrogen evolution. Later, Patra et al. 430 reported similar 2D sheets from covalent organic frameworks (2D-COFs) as metal-free catalysts for HER applications. 2D-MOFs are also being explored as active catalysts for the OER process. For example, Xu et al. 431 reported the preparation of 2D Co-MOF sheets using polyvinylpyrrolidone as a surfactant under mild solvothermal conditions. These novel 2D Co-MOFs displayed ultrathin nanosheets with many surface-based metal active sites, improving the overall OER performance.

Interestingly, experimental electrochemical measurement data showed that Co-MOF sheets offer a low overpotential ( i.e. , 263 mV at 10 mA cm −2 ). Similarly, Wang et al. 432 also reported that double-metal 2D-sheets (2D NiFe MOFs) consisting of a very ultrathin structure with a thickness of ∼10 nm further offer a low overpotential of 260 mV at 10 mA cm −2 . In other reports, Zhang et al. 433 successfully performed the OER process with ultrathin 2D-MOF sheets prepared via electrochemical and chemical exfoliation strategies.

Recent work on the catalytic activity of 2D-MOFs has also been reported in relation to the ORR and CO 2 RR because of their layered crystal structures and high-volume modifiable porous structures. For example, Dincă et al. 434 demonstrated that ultrathin layered conductive sheets of the 2D-MOF Ni 3 (HITP) 2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene) could actively be utilized as a catalyst in an alkaline medium for the ORR process. These 2D-MOF sheets show high stability while retaining 88% of the initial current density over 8 h at 0.77 V vs. RHE. In another report, through fabricating Co x Zn 2− x (bim) 4 2D-sheets as precursors, Zhao et al. 435 successfully synthesized cobalt nanodots (Co-NDs) with bimetallic Co x Zn 2− x (bim) 4 nanosheets encapsulating few-layer graphene (Co@FLG). For the CO 2 RR, a cobalt–porphyrin-containing 2D-MOF was achieved for the selective electrochemical reduction of CO 2 to CO with enhanced stability by Peidong Yang and co-workers. 436 The results further proved that these thin-film catalysts have the highest selectivity for CO ( i.e. , 76%) at −0.7 V vs. RHE with the little-to-no substantial decrease in activity over 7 h at −0.7 V vs. RHE, and 16 mL of CO was produced. Besides, like many other porous materials, 2D-MOFs were also shown to be a supporting platform for catalytic nanoparticles because of their high specific surface areas and favorable porosity distributions. To this end, an example can be noted from Wang et al. 437 reporting that fine porous MOF-5 nanosheets can be utilized to immobilize Pd nanoparticles.

5.4. Metal-based nanostructured materials

As discussed, catalysis is one of the main uses of metal-based nanostructured materials. A continuous increase in the demand for energy, the rapid depletion of conventional energy reservoirs, and rising concerns over the emission of CO 2 have increased the challenges and urgency in the energy field. 460 Metal-based nanostructured materials are extensively being explored to produce alternative clean and renewable energy sources. A range of metal-based nanomaterials has been evaluated and is under consideration for developing robust electrodes that can be effectively applied to water splitting, batteries, and solar cells.

High energy demands have led to more pressure to improve the performances of existing highly demanded lithium-ion batteries. Researchers have focused on improving their lifetimes, sizes, and safety. 462 Nanostructured metal-oxide-based materials are promising electrode materials for use in high-performance charge-storage devices. A metal-based nanostructured electrode is evaluated as both the anode and cathode to overcome the challenges of conventional electrodes. 463 In a conventional LIB, LiCoO 2 was used as the cathode material. Controlled morphology plays a crucial role in determining the performance of a material. Powder composed of spherical particles of LiNi 0.8 Co 0.2 O 2 showed a higher tap density compared to irregular particles and the material substantially improved the power density of secondary lithium batteries. 464 Hierarchical nanostructures of metal-based oxides (such as 3D hierarchical ZnCo 2 O 4 nanostructures) have emerged as a new trend for the development of high-capacity electrodes for lithium-ion batteries. 465 Since their commercialization by Sony in the early 1990s, LIBs have achieved tremendous success in bringing portable electronic devices to the market. However, their sustainable development on the grid-scale is hampered due to limited Li resources in nature, and this is causing a continuous increase in cost. 466 Sodium-ion batteries are in the spotlight to replace powerful lithium-ion batteries due to the widespread availability of sodium and its lower cost compared with lithium. 467 It is essential to note that, in terms of energy densities for SIBs, it is difficult to bypass LIBs because of the low standard electrochemical potential and higher weight of Na. SIBs could be proved to be ideal for those applications where cost is a critical factor compared to energy density. 466

SIBs also operate similarly to LIBs, based on an intercalation mechanism. SIBs also consist of cathode and anode electrodes separated through an electrolyte. During the charging process, sodium ions are extracted from the cathode and inserted into the anode via the electrolyte. In the discharging process, the electrons leave the anode through an external circuit to reach the cathode, providing electricity to the load, whereas Na + moves to the cathode during this process. The radius of Na + (1.02 Å) is greater than that of Li + (0.76 Å), making it challenging to intercalate into electrode materials. 468 Thus, appropriate electrode materials are required in which fast Na-ion insertion and extraction is possible. However, SIBs are suffering from a lack of appropriate electrode materials. It is important to develop electrode materials that have enough interstitial space within their crystallographic structures and better electrochemical performance. Among the various proposed electrode materials, Na x MO 2 layered transition-metal oxides (M = V, Fe, Cu, Co, Ni, Cr, Mn, and their combinations) are considered to be promising electrode materials for SIBs. Layered metal oxides are considered to be promising electrode materials due to their facile scalable synthesis, simple structures, appropriate operating potentials, and high capacities. 469,470 Large volume expansion and poor kinetics during the charge–discharge process can severely affect the cyclability and performance of SIBs. One of the effective strategies to deal with the mechanical stress triggered by large volume changes is the design of hollow or porous structures. In response, three-dimensional network-based Sb 2 O 3 @Sb composite anode materials can help to relieve the volume-change-related stress through their uniform porous networks and provide better transportation channels for Na + . 471

The large volume expansion of electrodes can also be buffered via designing 2D metal-oxide materials with large interlayer spacing. The ultrathin nanosheets provide high reversible capacity with enhanced cycling stability and contribute to providing reaction sites for electrons/ions, decreasing the diffusion distance, providing effective diffusion channels, and facilitating fast charge/discharge for sodium and lithium. 2D SnO nanosheet anodes were evaluated for SIBs. The capacity and cyclic stability improved, as the number of atomic SnO layers is decreased in the sheets. 472 Sb is a promising anode material, but during the sodiation/desodiation processes, huge volume expansion of 390% is observed, which hinders its practical use. Nanostructured Sb in the form of nanorod arrays with large interval spacing displays the great capacity to accommodate volume changes during cycling. 473 A comparison of various nanostructured metal-based electrodes for various charge storage purposes is shown in Table 3 . Overall, well-structured metal or metal-based oxide nanomaterials have the capacity to resolve current issues relating to charge storage devices.

Recently, an immense focus of research has been to produce H 2 fuel via water-splitting to replace conventional fossil fuels. This will help to eliminate emissions from the use of carbonaceous species. 484 Electrochemical method are considered simple water splitting approaches, as these methods only require an applied voltage and water as inputs to produce hydrogen fuel. 485 The coupling of solar irradiation to electrochemical water splitting has enhanced the performance and reduced the process cost. Due to these reasons, this has become a hot area of research. 486 During water electrolysis, H 2 is produced through the hydrogen evolution reaction at the cathode and O 2 is produced through the oxygen evolution reaction at the anode. However, water splitting is not so straightforward, and it requires an efficient catalyst that can facilitate the splitting of water. Metal- and metal-oxide-based catalysts are extensively being explored for water splitting. For the HER reaction, Pt-based catalysts are found to be suitable, whereas for OER reactions, Ir-/Ru-based compounds are found to be benchmark catalysts. Scarcity and high cost have limited the widespread use of these metals. The barrier of noble-metal cost can be mitigated through developing noble-metal nanostructured surfaces that produce more active sites or via depositing monolayers of noble metals on low-cost materials. The alloying of noble metals with other metals has enhanced site-specific activity. 484 At present, more focus is being placed on developing noble-metal-free catalysts for water splitting. 485 Usually, an efficient electrocatalyst is characterized by: 487 a low overpotential; high stability; low production costs; and high electrocatalytic activity.

The nano-structuring of catalysts is an effective tool to boost their surface areas. The electrolysis of water occurs at the surface of a catalyst, and nanostructured catalysts provide more active sites and the better diffusion of ions and electrolytes. 484 Non-noble metals that are under observation for the development of HER electrocatalysts include nickel (Ni), tungsten (W), iron (Fe), molybdenum (Mo), cobalt (Co), and copper (Cu). 487 For instance, a noble metal-free catalyst, carbon-decorated Co 3 O 4 nanoarrays on carbon paper, required a small overpotential of 370 mV to reach a current density of 10 mA cm −2 . It can maintain a current density of 100 mA cm −2 for 413.8 h and 86.8 h under alkaline and acidic conditions, respectively. 488

Metal-based semiconductor materials play a crucial role in a range of applications. For photoelectrochemical water splitting, the semiconductor material plays a central role in the solar-to-hydrogen conversion efficiency. Some critical features are prerequisites when it comes to selecting the right semiconductor material for the photoelectrochemical splitting of water: 489 an extraordinary capacity to absorb visible light; an appropriate bandgap; suitable valence and conduction band positions; commercial feasibility; and chemical stability.

For an ideal semiconductor for water splitting, the valence band and conduction band edge positions must straddle the oxidation and the reduction potentials of water. Metal oxides have received significant attention among semiconductors due to their wide band gap distributions, remarkable photo-electrochemical stabilities, and favorable band edge positions. 490 Semiconductor-based photoelectrodes become excited upon light irradiation, and electrons from the valence band move to the unoccupied conduction band. Some of the generated electrons at the cathode surface reduce protons to hydrogen gas, whereas holes at the photoanode produce oxygen gas via water splitting. 490 As a result, various nanostructured metal oxides can be used as photoelectrode materials, such as WO 3 , 491 Cu 2 O, 492 TiO 2 , 493 ZnO, 494 SnO 2 , 495 BiVO 4 , 496 and α-Fe 2 O 3 , 490 for the efficient splitting of water. As discussed, the nano-structuring of semiconductors can significantly impact the electrode photoelectrochemical performance during water splitting.

Metal-based nanomaterials have been used for the development of sensitive sensors. These metal-based sensors can replace the complex and expensive instruments that are conventionally used for the sensing of analytes. Metal-oxide-based sensors have the interesting characteristics of low detection limits, low cost, high sensitivity, and facile operation. 497 Mostly, semiconducting metal-oxide-based sensors are used for the sensing of toxic, flammable, and exhaust gases. Semiconductor metal oxides with a size in the range of 1–100 nm have been significantly investigated as gas sensors due to their size-dependent properties. The geometry and size of a nanomaterial can considerably affect the hole and electron movement in semiconductors. 498 The surface-to-volume ratio and surface area are substantially enhanced at the nanoscale level, and this is amazingly beneficial for sensing. Chemiresistive semiconducting metal oxides are potential candidates for gas sensing due to the following features: 499 rapid response times; fast recovery times; low cost; simple electronic interfaces; user-friendliness and low maintenance; and abilities to sense a wide range of gases.

Electrode materials decorated with metal- or metal-oxide-based nanostructured materials have shown better responses and selectivity for determining various analytes over conventional electrode materials. The nano-sized metal structures act as an electrocatalyst and electronic wires to provide rapid electron transfer between the transducers and analyte molecules. 500 The electrochemical redox reaction of H 2 O 2 can be improved via the thermally controlled anchoring of Pt NPs on the electrode surface. 501

Currently, researchers are not just concentrating on the development of randomly shaped nanomaterials; instead, they are very focused on and interested in the rational design of materials with controlled nano-architectures for boosting their performances for specific applications. As a result, extensive research has been carried out to develop metal-based materials with controlled dimensions to achieve better catalytic responses. Particle morphology is a crucial factor in the performance of nanomaterials for specific applications. Laifa Shen et al. rationally designed an electrode architecture via growing mesoporous NiCo 2 O 4 nanowire arrays on carbon textiles, which boosted the electrode performance ( Fig. 37 ). 474

The same materials with different morphologies can produce different outcomes. For instance, MnO 2 nanoflowers have provided high initial sodium-ion storage capacity compared with MnO 2 nanorods. 481 Radha Narayanan and Mostafa A. El-Sayed have analyzed various nanoscale morphologies of Pt, such as tetrahedral, cubic, and near-spherical nanoparticles. The highest rate constant is observed with tetrahedral nanoparticles and the lowest rate constant was observed with cubic nanoparticles, whereas spherical nanoparticles exhibited an intermediate rate constant during catalysis. 502 Xiaowei Xie et al. found that Co 3 O 4 nanorods show high activity compared to conventional Co 3 O 4 nanoparticles for the low-temperature oxidation of CO. 503 The catalytic activity of metal-based nanomaterials is strongly affected by their shape. 504 Shape-defined mesoporous materials (TiO 2 ) have shown superior photoanode activities ( Fig. 38 ). 505 As a result, in the literature, several nanostructured morphologies of metal-based materials, such as nanotubes, 506,507 nanorods, 508,509 nanoflowers, 510 nanosheets, 511 nanowires, 512 nanocubes, 513 nanospheres, 514,515 nanocages, 516 and nanoboxes, 517 have been reported for a range of applications.

Hollow nanostructures have surfaced as an amazing class of nanostructured material, and they have received significant attention from researchers. Hollow nanostructures have the unique features of: 518,519 low density; abundant inner void spaces; large surface areas; and the ability to act as nanoscale containers with high loading capacity, nanoreactors, and nanocarriers.

Various metal-based hollow nanostructures, such as hollow SnO 2 , 520 hollow palladium nanocrystals, 521 Co–Mn mixed oxide double-shell hollow spheres, 521 hollow Cu 2 O nanocages, 522 three-dimensional hollow SnO 2 @TiO 2 spheres, 523 hollow ZnO/Co 3 O 4 nano-heterostructure, 524 triple-shell hollow α-Fe 2 O 3 , 525 and hierarchical hollow Mn-doped Ni(OH) 2 nanostructures, 526 have been developed for various applications. The presence of nanoscale hollow interiors and functional shells imparts them with great potential for gas sensing, catalysis, biomedicine, energy storage, and conversion. 519

From this discussion, it can be concluded that metal-based nanostructured materials have great potential compared to their bulk counterparts. The conversion of materials to the nanoscale is not enough to achieve high performance with better selectivity. Now, research is switching from conventional nanomaterials to more advanced and smartly designed nanomaterials. In modern research, nanomaterials are being designed with better-controlled morphologies and regulated features.

5.5. Core–shell nanoparticles

A spherical nanoparticle core–shell nanostructure is a practical way to introduce multiple functionalities on the nanoscopic length scale. 528 The properties arising from the core or shell can be different, and these properties can be tuned via controlling the ratio of the constituent materials. The shape, size, and composition play a critical role in tuning the core–shell nanoparticle properties. 529 The shell material can help to improve the chemical and thermal stabilities of the core material. The core–shell design has become effective where an inexpensive material cannot be used directly due to its instability or easily oxidizable nature. The core can consist of an easily oxidizable inexpensive metal, whereas the shell might consist of noble metals, oxides, polymers, or silica. 530 For instance, magnetic nanoparticles when prepared can be sensitive toward air, acids, and bases. Magnetic nanoparticles can be protected via coating with organic or inorganic shells. 528

Core–shell metal nanoparticles are an emerging nanostructured material with great potential in the fields of energy and catalysis. 531 The first report of core–shell nanoparticles (2007) for supercapacitor applications consisted of a polyaniline/multi-walled-carbon-nanotube composite (PANI/MWNTs). 532 Metal-based core–shell structured nanoparticles have shown enhanced catalytic performance due to their shape-controlled properties. 533 Ming-Yu Kuo et al. developed Au@Cu 2 O core–shell particles with controllable shell thicknesses that acted as a dual-functional catalyst. The shell thickness of Cu 2 O increased with an increasing concentration of Cu 2+ precursor. The thicknesses of the shells of Au@Cu 2 O-1.5 (12.2 ± 1.7 nm), Au@Cu 2 O-2 (13.2 ± 1.8 nm), Au@Cu 2 O-3 (18.2 ± 2.2 nm), and Au@Cu 2 O-4 (20.8 ± 2.5 nm) due to various concentrations are shown in Fig. 40 . 534 A NiO@SiO 2 core–shell catalyst provided a higher yield of acrylic acid from acetylene hydroxycarbonylation. 535 Core–shell architecture can be used to prevent active metal nanoparticles from oxidation during operation. For instance, a plasmonic photocatalyst was developed that consisted of silver nanoparticles embedded in titanium dioxide. The direct contact of Ag with TiO 2 could lead to its oxidization; this is prevented via developing core–shell architecture in which Ag is used as the core and SiO 2 is used as a shell to protect it. 536 Another excellent option is to replace an expensive core with a non-noble metal to reduce the core–shell cost while using a thin layer of a noble metal that consumes a small amount of metal as the shell. This will ensure the prolonged stability of the catalyst during operation. 533 Overall, core–shell morphologies provide better catalytic activity due to the synergistic effect of the metallic core–shell components. 152

Among the several classes of nanomaterials, core–shell nanoparticles are found to be more promising for different biomedical applications. For instance, magnetic nanoparticles are considered to be useful for biomedical applications due to the following reasons: (a) aggregation is prevented due to superparamagnetism; (b) delivery and separation can be controlled using an external magnetic field; (c) they can be appropriately dispersed; and (d) there is the possibility of functionalization. A range of magnetic nanoparticles is available, such as NiO, Ni, Co, and Mn 3 O 4 . The most famous example is iron oxide, but uncoated iron oxides are unstable under physiological conditions. This may result in controlled drug delivery failure due to improper ligand surface binding and the promotion of the formation of harmful free radicals. Therefore, the formation of shells around magnetic nanoparticles has tremendous significance for biomedical applications. 537 One of the approaches is to use gold shells on magnetic nanoparticles. Au NPs are also called surface plasmons and they substantially enhanced the absorption of light in the visible and near-infrared regions. Thus, coating magnetic nanoparticles with a Au shell can result in a core–shell nanostructure that displays both optical and magnetic functionality in combination. 529

Numerous biocompatible core–shell nanoparticles are being developed for photothermal therapy, as core–shell materials are found to be useful for photothermal therapy. Hui Wang et al. have developed bifunctional core–shell nanoparticles for dual-modal imaging-guided photothermal therapy. The core–shell nanoparticles consist of a magnetic ∼9.1 nm core of Fe 3 O 4 covered by an approximately 3.4 nm fluorescent carbon shell. The Fe 3 O 4 core leads to superparamagnetic behavior, whereas the carbon shell provides near-infrared (NIR) fluorescence properties. The bifunctional nanoparticles have shown dual-modal imaging capacity both in vivo and in vitro . The iron oxide–carbon core–shell nanoparticles absorbed and converted near-infrared light to heat, facilitating photothermal therapy. 538 Au-Based core–shell structures are also being prepared for photothermal therapy. Bulk gold is biocompatible, but Au NPs can accumulate in the spleen and liver, causing severe toxicity. Koo Chul Kwon et al. have developed Au-NP-based core–shell structures that did not result in any gross or histological lesions in the major organs of mice, which revealed that this is a potent and safe agent for photothermal cancer therapy. The core–shell nanoparticles consisted of proteinticle/gold (PGCS-NP) and were developed via proteinticle surface engineering. PGCS-NP was injected intravenously into mice with tumors, and the injected core–shell nanoparticles successfully reached the EGFR-expressing tumor cells. The tumor size was significantly reduced upon exposure to near-infrared laser irradiation ( Fig. 41 ). No accumulation of Au NPs was observed in the mice organs, which indicated that PGCS-NP disassembled into many tiny gold dots, which were easily excreted by the kidneys and liver without causing any toxicity. 539 In another example, multifunctional Au@graphene oxide nanocolloid core@shell nanoparticles were developed, in which the core and shell consisted of gold and a graphene oxide nanocolloid, respectively. The developed core–shell structure showed multifunctional properties, allowing Raman bioimaging and photothermal/photodynamic therapy with low toxicity. 540 Apart from this, numerous other core–shell nanoparticles, such as polydopamine–mesoporous silica core–shell nanoparticles, 541 AuPd@PVP core–shell nanoparticles, 542 Au@Cu 2− x S core–shell nanoparticles, 543 bismuth sulfide@mesoporous silica core–shell nanoparticles, 544 and Ag@S-nitrosothiol core–shell nanoparticles, have been used for photothermal therapy. 545

Due to their unique features and the combination of properties from the shell and core, these core–shell nanoparticles have received considerable interest in many fields, ranging from materials chemistry to the biomedical field. For electrochemical reactions, the core–shell structure conductivity can be enhanced via conducting polymers, carbon materials, and metals. Core–shell nanoparticles as electrode materials showed better performance compared to single components. Most of the core materials are prepared via hydrothermal methods, and shells can be prepared via hydrothermal or electrodeposition methods. 546 Even though significant progress has been made relating to the synthesis methods of core–shell materials, a major challenge is the high-quality production of core–shell materials in more effective ways for required applications, specifically biomedical applications.

6. Challenges and future perspectives

(a) The presence of defects in nanomaterials can affect their performance and their inherent characteristics can be compromised. For instance, carbon nanotubes are one of the strongest materials that are known. However, impurities, discontinuous tube lengths, defects, and random orientations can substantially impair the tensile strength of carbon nanotubes. 547

(b) The synthesis of nanomaterials through cost-effective routes is another major challenge. High-quality nanomaterials are generally produced using sophisticated instrumentation and harsh conditions, limiting their large-scale production. This issue is more critical for the synthesis of 2D nanomaterials. Most of the methods that have been adopted for large-scale production are low cost, and these methods generally produce materials with defects that are of poor quality. The controlled synthesis of nanomaterials is still a challenging job. For example, a crucial challenge associated with carbon nanotube synthesis is to achieve chiral selectivity, conductivity, and precisely controlled diameters. 548,549 Obtaining structurally pure nanomaterials is the only way to achieve the theoretically calculated characteristics described in the literature. More focused efforts are required to develop new synthesis methods that overcome the challenges associated with conventional methods.

(c) The agglomeration of particles at the nanoscale level is an inherent issue that substantially damages performance in relevant fields. Most nanomaterials start to agglomerate when they encounter each other. The process of agglomeration may be due to physical entanglement, electrostatic interactions, or high surface energy. 550 CNTs undergo van der Waals interactions and form bundles, making it difficult to align or properly disperse them in polymer matrices. 159 Similarly, graphene agglomeration is triggered by the basal planes of graphene sheets due to π–π interactions and van der Waals forces. Due to severe agglomeration, the high surface areas and other unique graphene features are compromised. These challenges hinder the practical application of high-throughput electrode materials or composite materials for various applications. 551

(d) The efficiency of nanomaterials can be tuned via developing 3D architectures. 3D architectures have been tried with several nanomaterials, such as graphene, to improve their inherent features. 3D architectures of 2D graphene have provided high specific surface areas and fast mass and electron transport kinetics. This has become possible due to the combination of the exceptional intrinsic properties of graphene and 3D porous structures. 194,552 The combination of graphene and CNT assemblies into 3-D architectures has emerged as the most investigated nanotechnology research area. Porous architectures of other nanomaterials can be developed to enhance their catalysis performance through providing nanomaterial interior availability.

(e) 2D ultrathin materials are an outstanding class of nanomaterial with promising theoretical properties; however, very little experimental evaluation of these materials has been done, apart from the case of graphene. The synthesis and stability of 2D ultrathin materials are some of the major challenges associated with them. In the future, more focus is anticipated to be placed on their synthesis and practical utilization.

(f) Nanomaterial utilization in industry is being increased, and there is also demand for nanoscale material production at higher rates. Moreover, nanotechnology research has vast horizons; the exploration of new nanomaterials with fascinating features will continue and, in the future, more areas will be discovered. One of the significant concerns relating to nanomaterials that cannot be overlooked is their toxicity, which is still poorly understood, and this is a serious concern relating to their environmental, domestic, and industrial use. The extent to which nanoparticle-based materials can contribute to cellular toxicity is unclear. 553 There is a need for the scientific community to put efforts into reducing the knowledge gap between the rapid development of nanomaterials and their possible in vivo toxicity. A proper and systematic understanding of the interaction of nanomaterials with cells, tissues, and proteins is critical for the safe design and commercialization of nanotechnology. 14

The future of advanced technology is linked with advancements in the field of nanotechnology. The dream of clean energy production is becoming possible with the advancement of nanomaterial-based engineering strategies. These materials have shown promising results, leading to new generations of hydrogen fuel cells and solar cells, acting as efficient catalysts for water splitting, and showing excellent capacity for hydrogen storage. Nanomaterials have a great future in the field of nanomedicine. Nanocarriers can be used for the delivery of therapeutic molecules.

7. Conclusions

Conflicts of interest, acknowledgements.

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• Open access
• Published: 18 March 2024

Dietary microalgal-fabricated selenium nanoparticles improve Nile tilapia biochemical indices, immune-related gene expression, and intestinal immunity

• Eman Zahran   ORCID: orcid.org/0000-0003-2212-3688 1   na1 ,
• Samia Elbahnaswy   ORCID: orcid.org/0000-0003-1414-9407 1   na1 ,
• Fatma Ahmed   ORCID: orcid.org/0000-0003-2767-2010 2 ,
• Engy Risha 3 ,
• Abdallah Tageldein Mansour 4 , 5 ,
• Arwa sultan Alqahtani 6 ,
• Walaa Awadin 7 &
• Mahmoud G. El Sebaei 8 , 9  

BMC Veterinary Research volume  20 , Article number:  107 ( 2024 ) Cite this article

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Feed supplements, including essential trace elements are believed to play an important role in augmenting fish immune response. In this context, selenium nanoparticles (SeNPs) in fish diets via a green biosynthesis strategy have attracted considerable interest. In this investigation, selenium nanoparticles (SeNPs, 79.26 nm) synthesized from the green microalga Pediastrum boryanum were incorporated into Nile tilapia diets to explore its beneficial effects on the immune defense and intestinal integrity, in comparison with control basal diets containing inorganic Se source. Nile tilapia (No. 180, 54–57 g) were fed on three formulated diets at concentrations of 0, 0.75, and 1.5 mg/kg of SeNPs for 8 weeks. After the trial completion, tissue bioaccumulation, biochemical indices, antioxidant and pro-inflammatory cytokine-related genes, and intestinal histological examination were analyzed.

Our finding revealed that dietary SeNPs significantly decreased ( P  < 0.05) serum alkaline phosphatase (ALP), lactate dehydrogenase (LDH), and cholesterol, while increasing ( P  < 0.05) high-density lipoproteins (HDL). The Se concentration in the muscle tissues showed a dose-dependent increase. SeNPs at a dose of 1.5 mg/kg significantly upregulated intestinal interleukin 8 ( IL-8 ) and interleukin 1 beta (IL-1β) gene transcription compared with the control diet. Glutathione reductase ( GSR ) and glutathione synthetase ( GSS ) genes were significantly upregulated in both SeNPs-supplemented groups compared with the control. No apoptotic changes or cell damages were observed as indicated by proliferating cell nuclear antigen ( PCNA ) and caspase -3 gene expression and evidenced histopathologically. SeNPs supplementation positively affects mucin-producing goblet cells (GCs), particularly at dose of 1.5 mg/kg.

Therefore, these results suggest that Green synthesized SeNPs supplementation has promising effects on enhancing Nile tilapia immunity and maintaining their intestinal health.

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Introduction

With aquaculture intensification and the increasing probability of disease outbreaks, new strategies have been implemented to overcome these challenges [ 1 ]. One of these strategies is to use feed supplementation, and recently nanoparticles (NPs) dietary supplementations have been paid great attention owing to their greater benefits for fish [ 2 ]. Green biosynthesis of metals NPs is more promising than the chemically synthesized ones since a biogenic approach requires non-toxic solvents, low temperatures, and inexpensive biodegradable reducing agents. In general, nanoparticles (NPs) produced by biological organisms show better physicochemical characteristics, such as smaller size, large surface area, high stability, and minimal cytotoxicity [ 3 , 4 ]. In addition, they are not harmful to the environment because living organisms, such as fungi, algae, bacteria, and plants, can reduce and stabilize metals used as a method of detoxification [ 5 , 6 ].

Selenium (Se) is a dietary trace mineral involved in the metabolism of living organisms. The optimal necessities of Se fluctuated from 0.15 to 0.7 mg/ kg in many fish species [ 7 ]. It plays a pivotal role in antioxidant resistance and the regulation of metabolic pathways such as thyroid hormones, cellular growth, and immune capacity [ 8 ]. It also acts as a shelter against oxidative stress because it is essential for the production of selenoenzymes such as glutathione peroxidase (GPx) and selenocysteine [ 9 ]. The primary consequences of selenium deficiency in fish include increased susceptibility to pathogens, growth scarcity, immunosuppression, and inflammatory diseases [ 10 , 11 ]. However, high Se concentrations and long-term Se supplementation are associated with toxicity [ 12 ]. In the natural environment, selenium is present in inorganic forms, such as selenite Se (IV) and selenate Se (VI) ions, and as an organic species with direct Se-C bonds (methylated compounds, selenoamino acids, and selenoproteins) [ 13 ]. This immensity of Se’s benefits rendered its incorporation into fish diets via green biosynthesis as nano-selenium essential [ 5 , 13 , 14 ]. Moreover, as a component of proteins such as selenoproteins, Se can improve the process of digestion, leading to an increase in goblet cells, which is linked to mucosal immunity [ 15 ]. Mucosal barriers and the antioxidant system have a pivotal roles of aquatic animals' disease resistance and are classified as components of the innate immune system [ 16 ].

SeNPs Biosynthesis using plants had been investigated in many literature, however, using microalgae to green synthesize SeNPs is more preferable, considering their rapid growth and ability to double their mass faster than higher plants, besides; their capabilities to reduce metal ions [ 17 , 18 ], due to the formation of biomolecular complexes with metal-chelating biomolecules in algal extracts (e.g., polysaccharides, peptides, and pigments) for capping metal nanoparticles [ 19 , 20 ]. In this context, members of the genus Pediastrum (Sphaeropleales, Hydrodictyaceae), unicellular and colonial chlorophytes, are promising microalgae for biotechnological, food, industrial, and pharmaceutical applications [ 21 , 22 ]. The most widely distributed species in eutrophic freshwater and sediments from the Cretaceous of Egypt is Pediastrum boryanum ( P. boryanum, Turpin) [ 23 ]. The green microalga P. boryanum produces higher levels of secondary metabolites, such as carotenoids, polyunsaturated fatty acids, vitamins, carotenoid pigments, and polyphenols, demonstrating a notable inhibitory effect on lipid peroxidation [ 24 ]. Prior studies have examined the impacts of SeNPs produced from various microalgae strains, including Spirulina platensis-SeNPs and numerous cyanobacterial strains [ 25 , 26 ]. Nevertheless, no research has been conducted on the SeNPs synthesized using the green microalga P. boryanum. Therefore, it is noteworthy to explore how might these NPs-based microalgae contribute to augment Nile tilapia immune response and thus control diseases in tilapia farming system.

The farmed Nile tilapia, Oreochromis niloticus, mainly contributes to the animal protein supply and food safety for millions of Egyptian populations, as this fish was produced nationally by 66% of the total cultured fish species and 43% of the total fish consumption in Egypt in 2019 [ 27 , 28 ]. Therefore, we aimed to explore the potential impact of P. boryanum-derived biosynthesized SeNPs on Nile tilapia’s immune response via selected biomarkers, including serum biochemical parameters, and sets of selected functional related-genes expression, besides, intestinal integrity. To the best of our knowledge, P. boryanum is a novel green microalga that has not previously been studied in fish.

Material and methods

Ethical approval.

The experiment was conducted following the protocol involving the use of animals that were approved by the Mansoura University Animal Care and Use Committee (VM.R.23.12.135). All fish handling procedures and regulations followed the ARRIVE guidelines for Animal Care and Use. Furthermore, all relevant organisational and government rules and regulations governing the ethical use of experimental animals were followed. Written informed owner consent has been obtained in this study.

Preparation of Pediastrum boryanum extract

The selected microalga, P. boryanum powder (National Research Centre, Cairo, Egypt), was subjected to extraction according to previously described methods [ 29 ], with a slight modification to be convenient for the newly invented device, namely the El-Ghamry and El-Khateeb Bio-Nano Apparatus. This instrument consists of ten units, starting from the solvent unit to the extraction, biosynthesis, and control panel units, as detailed in a previous study [ 30 ]. Briefly, the microalgal plant (100 g) was dissolved in the first solvent unit containing 1 L distilled water (DW) using a magnetic stirrer (Lss Egypt, Cairo, Egypt). The dissolved solution was boiled in the extraction unit at 70 °C for 2 h. The P. boryanum extract was filtered through Whatman filter paper using a Büchner funnel. The volume of the filtrates was adjusted to 1 L in a volumetric bottle with deionized DW. Finally, part of this extract was directly transferred to the biosynthesis unit to further generate Se nanoparticles.

Synthesis of selenium nanoparticles (SeNPs)

The biological synthesis of the metal nanoparticles was performed according to previously reported procedures [ 6 , 31 ] with some modifications to fit the newly invented apparatus [ 30 ]. This step was performed in the biosynthesis unit, in which an aqueous solution of 1 L of selenium (IV) oxide (Sublimed, Merck, Darmstadt, Germany) was added slowly dropwise to 1 L of the prepared algal extract solution under magnetic stirring. After continuous stirring of the mixture for an extra two hours at room temperature, the mixed solution was automatically transferred to the irradiation unit for UV irradiation using a reduction factor lamp (Vilber Lourmat-6. LC, France) at a wavelength (λ = 254 nm) for 20 min, according to a previously reported method [ 32 ]. The synthesized nanoparticles were filtered through Whatman no. 1 filter paper (Whatman International Ltd., Kent, UK) and then transferred to the product storage unit, where the final product of Se nanoparticles was stored at − 18 °C for further experiments.

Characterization of SeNPs

The morphological features ( e.g ., particle size, shape, and surface nature) of the SeNPs were examined using transmission electron microscopy (TEM) (JEOL TEM-2100, Tokyo, Japan) at the Electron Microscope Unit, Mansoura University, Egypt, as described in a previous study [ 6 ]. A drop of the prepared solution was spread onto a carbon-coated copper grid, which was then dried at room temperature and photographed under a microscope at 200 nm magnification value. The samples were subjected to crystallographic analysis using powder X-ray diffraction (XRD). Scanning mode X-ray diffraction patterns were captured using a Bruker D2 Phaser analytical instrument set at 30 kV and 10 mA current with Cu K radiation (λ = 1.54060 Ω). The intensities ranging from 5° to 79.93° were measured at two angles. A comparison was made between the diffraction intensities and the standard JCPDS files. The surface charge and stability of the prepared selenium nanoparticles were characterized using a zeta potential analyzer (Malvern Zetasizer® Version 2.3, Kassel, Germany) in the same Electron Microscope Unit, which depends on electrophoretic light scattering [ 33 ].

Experimental rearing and feeding regimes of Nile tilapia

The basal ingredients of the fish feed were prepared at the laboratory of the Department of Nutrition, Faculty of Veterinary Medicine, Mansoura University. The SeNPs were individually incorporated into the basal diet at three different concentrations, 0, 0.75, and 1.5 mg/kg. The three diets were formulated according to NRC [ 34 ], as presented in Table  1 . The experimental groups were as follows: the control group was fed a basal diet (containing Se in the mineral mix (inorganic form Na 2 Seo 3 as 0.2 mg/Kg diet), SeNPs 0.75 group was fed a mineral premix Se- free diet supplemented with SeNPs at 0.75 mg/kg body weight, and SeNPs 1.5 group was fed a mineral premix Se- free diet supplemented with SeNPs at 1.5 mg/kg body weight. SeNPs suspensions at concentrations of 0.75 and 1.5 SeNPs mg/kg feed were gradually added and thoroughly mixed with the ingredients of the basal diet. All dietary components were mixed with gelatin, and sterilized water was added until the formation of a stiff paste. The paste was pelleted into 3-mm-diameter pellets using a meat mincer (ME605131 1600-Watt, Moulinex, Groupe SEB, France). Finally, the pellets were oven-dried at 50 °C for 24 h before being placed in a plastic bag and stored at 4 °C until use.

A total of 180 apparently healthy Nile tilapia, with an average body weight of 54–57 g, were cultured in a private fish farm, Lake Manzala, Bahr El-Baqar drain, Egypt, and were used in this study. The fish were randomly distributed into three experimental groups in triplicate (20 fish/hapa). They were allocated into nine hapas (200 × 500 × 100 cm 3 , 10 m 3 ) for each experiment, where the water quality parameters were monitored biweekly at a temperature of 26 °C, the dissolved oxygen ranged from to 6.7–6.9 mg/liter, and the pH level was adjusted to 7.3 ± 0.2. The water exchange (10%) was performed daily. During the acclimatization period, fish were fed a basal diet twice (at 9 a.m. and 4 p.m., respectively) per day at 3% of their biomass (on a dry matter basis). The fish were weighed every 2 weeks to readjust the feeding quantity. The experiment lasted for 8 weeks.

Serum and tissue sampling

After the fish were anesthetized with clove oil at 60 mg/L, blood samples were collected from the caudal vessels of six fish per group using non-heparinized disposable syringes for serum separation, centrifuged at 1198 × g for 15 min at 4 °C, and stored at – 20 °C for analysis of biochemical parameters and lipid profile. For digestive enzyme analysis, part of the intestinal tissue was separated, washed many times with cold 0.9% NaCl solution, and stored at – 80 °C. Next, 100 mg of the intestine was collected in Eppendorf tubes containing 500 µL of RNA later® (Sigma) solution and stored at – 20 °C for estimation of gene expression. In addition, intestinal samples were dissected and placed in a 10% neutral buffered formaldehyde solution for histomorphometric analyses.

Determination of serum biochemical assays

Serum alkaline phosphatase (ALP, Elitech Group Inc., 55,230, Envoy500, California, USA) and lactate dehydrogenase (LDH, Elitech Group Inc., 55,395, USA) activities were quantitatively determined according to the manufacturers’ instructions. In addition, the lipoprotein profile, including cholesterol, triglycerides (TG), low-density lipoproteins (LDL), and high-density lipoproteins (HDL), were measured calorimetrically using diagnostic reagent kits (SPINREACT Diagnostics, S.A./S.A.U Ctra., Santa Coloma, Spain), according to the standard protocol for their specific pamphlets [ 35 , 36 , 37 , 38 ].

Estimation of Se mineral contents in fish diets and musculature

The Se content of the test diets and muscles was assessed by the digestion of samples in nitric acid (AOAC 1998). Samples were collected at random and dried for 48 h at 105 °C. The samples were digested with concentrated H 2 SO 4 . Se concentrations in the fish musculature were determined using an atomic absorption spectrophotometer (PG990, UK) following the standard method [ 39 ].

RNA extraction, complementary DNA synthesis, and qRT-PCR

Total RNA was manually extracted from 100 mg of each intestinal sample from each group (control, SeNPs0.75, SeNPs1.5) using a handheld homogenizer to homogenize the tissue immersed in one mL Genzol™ (Geneaid Biotech Ltd., Taiwan) without DNase treatment. The pellet was dissolved in TE buffer (pH 8.0) as described previously [ 40 ]. The RNA quantity was estimated using a NanoDrop spectrophotometer (Q5000/Quawell, Massachusetts, USA). Complementary DNA (cDNA) containing 1 μg of total RNA was synthesized using a TOPscript™ RT DryMIX(dT18) cDNA Synthesis Kit (Enzynomics Co Ltd, Daejeon, Republic of Korea) according to the manufacturer's protocol. The specific primers used to amplify the selected genes of Nile tilapia with antioxidant genes: Glutathione peroxidase ( GPx ), Glutathione-S-transferase ( GST ), Glutathione reductase ( GSR ), and Glutathione synthetase ( GSS ); pro-inflammatory genes, Tumor necrosis factor-alpha ( TNF-α ), Interleukin 8 ( IL-8 ), and Interleukin 1 beta ( IL-1β ); anti-inflammatory genes, Transforming Growth Factor-β ( TGF-β ); apoptotic and regulatory-related genes, proliferating cell nuclear antigen ( PCNA ) and caspase -3, in addition to the β-actin as a housekeeping gene were described elsewhere [ 41 , 42 , 43 ]. The QuantStudio™ 1 Real-Time PCR System (Applied Biosystems™ Thermo Fisher Scientific, USA) was used to quantify the expression of genes using Solg™ 2X Real-Time PCR Smart mix (Including SYBR® Green) (SolGent Co., Ltd. Yuseong-gu, Daejeon, Korea). The thermocycling conditions were as follows: 95 °C for 20 s, followed by 40 cycles of denaturation at 60 °C for 40 s, and elongation at 72 °C for 30 s.

Histochemical differentiation of the intestinal mucin-producing goblet cells

The intestinal tissue samples were fixed in 10% neutral buffered formalin for 24 h, embedded in paraffin wax, and sectioned at 5 µm. Selected slides were routinely stained with Hematoxylin and Eosin (H&E), according to KS Suvarna, C Layton and JD Bancroft [ 44 ] and were examined under a light microscope (Olympus CX 31). Goblet cells (GCs) in the intestine were semi-quantified as previously described by Ahmed et al. [ 45 ], with minor modifications. In brief, intestinal samples were stained with Alcian Blue AB (pH 2.5) and Periodic-Acid Schiff (PAS) double staining for GCs differentiation, according to Padra et al. [ 46 ]. Mucin-free and mucin-filled GCs in intact villi along 5000 µm length of the mucosal epithelium were counted in triplicate slides per treated group [ 47 ]. The differential count of mucin-producing GCs depended on their visible color under a light microscope (Olympus CX 31). Acid mucin-producing GCs were stained blue with AB (pH 2.5), neutral mucin-producing GCs were stained pink with PAS, mixed mucin-producing GCs were double stained and appeared purple, whereas mucin-free GCs were negatively stained. Triplicate blinded fields (40 ×) per examined section were surveyed, and the obtained data were expressed as mean percentage ± SD.

Statistical analysis

The data were first checked for normality and homogeneity using Kolmogorov–Smirnov and Levene's tests, respectively. One-way analysis of variance (ANOVA) was used to determine the significance of the group variables using GraphPad® statistics package version 8.4.2. (GraphPad Software, Inc., USA). Individual fold-change values were normalized and anchored to the lowest value recorded in each data set before Log2 transformed, as previously described [ 48 ]. The significance level was set at P  < 0.05 (*), P  < 0.01 (**), and P  < 0.001 (***). All data are presented as mean ± SEM.

The morphology and size of the prepared selenium nanoparticles from P. boryanum were determined using TEM. The formed nanoparticles exhibited spherical and tetragonal shapes at a higher spatial resolution (200 nm). The size of the selenium particles ranged from 72.16 nm to 89.45 nm (Fig.  1 A). The selenium particles synthesized with the algal extract had zeta potentials of − 11.7 mV (Fig.  1 B), showing a higher degree of stability, as nanoparticles had zeta potential values greater than − 25 mV. Additionally, the results were interpreted. The nature of a double layer of ions on the surface of the nanoparticles allows more diffusion into the solution. The XRD pattern of the selenium dioxide nanoparticles (Fig.  1 C) shows peaks that correspond to the atomic planes in the crystal structure. The predominant phase of Se dioxide is α-SeO 2 , which has a monoclinic crystal structure. α-SeO 2 nanoparticles exhibit several peaks, the strongest peaks in the XRD pattern of α-SeO 2 nanoparticles are at 21°, 29°, and 34 suggesting that the α-SeO 2 nanoparticles in the sample are orientated parallel to the sample surface, with planes (110), (121), (021), and (201).

Transmission electron microscope micrographs and zeta potential graphs of the prepared selenium nanoparticles of the Pediastrum boryanum extract. A TEM micrographs and size distributions for biosynthesized selenium nanoparticles by P. boryanum extract at a 200 nm magnification value. B Zeta potential of the prepared nano-selenium synthesized by P. boryanum extract. C  X-ray diffraction (XRD) pattern of SeNPs

Selenium content in fish diet and musculature

Selenium content in fish diets was determined to be 0.22 mg/kg (a commercial diet used as a inorganic Se), 0.79 mg/kg (SeNPs 0.75 ), and 1.8 mg/kg (SeNPs 1.5 ). These values were marginally higher than the specified concentrations, owing to the presence of trace amounts of Se in these ingredients. Se content in fish musculature was increased by SeNPs supplementation in the fish diets (Fig.  2 ). It exhibited a dose-related increment, being significantly higher in fish fed 0.75 and 1.5 mg ( P  < 0.05) compared with the values of fish fed the control diet.

Se content in muscle tissue of Nile tilapia fed on different levels of SeNPs. Data were represented as Mean ± SEM ( n  = 3). Values with a different letter superscript are significantly different between groups (ANOVA with post hoc Tukey test, * P (< 0.05), ** P (< 0.01). *** P (< 0.001)

Serum biochemical indices

ALP enzyme activity was significantly lower (over 1.5-fold decrease) in fish fed both doses of SeNPs (0.75 and 1.5 mg/kg Body weight) than in fish fed the control diet ( P  < 0.05). No significant alterations were observed in the activity of ALP between the two levels of SeNPs. Additionally, LDH enzyme had a notably lower value in the group of fish fed 1.5 mg SeNPs/kg compared to fish fed the basal diet (nearly threefold decrease, P  < 0.05) and SeNPs 0.75 fish group (over threefold decrease, P  < 0.01), without statistical changes between the latter ( P  > 0.05) (Fig.  3 A). Cholesterol levels were significantly lower in the SeNPs 0.75 & 1.5 fish groups (nearly threefold decrease, P  < 0.05, and 1.5-fold decrease, respectively). A marked decrease in cholesterol levels was observed in the SeNPs 0.75 fish group compared to the SeNPs 1.5 fish group (0.5- fold decrease, P  < 0.05). No significant difference was observed in the values of Triglyceride (TG) between the control and SeNPs-treated groups (Fig.  3 B). A notable increase in HDL levels was observed in the SeNPs 1.5 fish group compared to the control (1.5-fold increase, P  < 0.01) and SeNPs 0.75 fish groups (nearly twofold increase, P  < 0.001). Nevertheless, there was a notable decrease in LDL levels in the SeNPs 1.5 fish group compared to that in the SeNPs 0.75 fish group (nearly twofold increase, P  < 0.05). However, no notable changes in LDL levels were detected compared with the control diet (Fig.  3 B).

Serum biochemical indices of Nile tilapia supplemented with biosynthesized selenium nanoparticles (0, 0.75, and 1.5 mg SeNPs/kg) for 8 weeks ( N  = 6). A Liver enzymes activity, alkaline phosphatase (ALP) and lactate dehydrogenase (LDH). B Lipid profile, cholesterol, TG, LDL, and HDL. Data were expressed as Mean ± SEM. Values with a different letter superscript are significantly different between groups. Asterisks indicate levels of significance (ANOVA with post hoc Tukey test, * P  < 0.05; ** P  < 0.01; *** P  < 0.001)

Intestinal genes expression

SeNPs 1.5 fish group exhibited a significant upregulation in the transcription of intestinal IL-1β and IL-8 genes (ninefold increase, P  < 0.01; tenfold increase, P  < 0.05), respectively, compared to the control fish group (Fig.  4 ). Furthermore, IL-1β was significantly upregulated SeNPs 1.5 fish group compared to that in the SeNPs 0.75 fish group (threefold increase, P  < 0.05) (Fig.  4 A). However, TNF-α and TGF-β gene transcription showed no notable changes ( P  > 0.05) between the SeNPs and control fish groups (Fig.  4 A). Concerning the antioxidant genes, the SeNPs 1.5 fish group displayed significant upregulation of the intestinal GST (sevenfold increase, P  < 0.01) and GPx (eightfold increase, P  < 0.05) genes compared to fish fed the control diet. However, no significant changes were observed in other groups (Fig.  4 B). Interestingly, the expression of intestinal GSS and GSR were in similar trend, where significant upregulations were noticed in SeNPs 0.75 & 1.5 fish groups (fourfold increase, P  < 0.05; sixfold increase, P  < 0.01) in case of GSS and (sevenfold increase, P  < 0.05; tenfold increase, P  < 0.01) compared to the control (Fig.  4 B). PCNA and caspase -3 gene expression exhibited no significant changes ( P  > 0.05) among groups (Fig.  4 C).

Comparative intestinal gene expression of ( A ) pro-inflammatory genes (e.g., TNF-α , IL-8 , and IL-1β ), and anti-inflammatory gene (TGF-β ), ( B ) antioxidant genes (e.g., GPx , GST , GSR , and GSS ), and ( C ) regulatory and apoptotic-related genes (PCNA and caspse-3) of Nile tilapia fed biosynthesized SeNPs (0, 0.75, and 1.5 mg SeNPs/kg) for eight weeks ( N  = 6). The qPCR detected transcript levels were normalized to the expression of a reference gene, Nile tilapia β-actin , and presented as Mean ± SEM. The values with a different letter superscript are significantly different between groups. Asterisks indicate levels of significance (ANOVA with post hoc Tukey test, * P  < 0.05; ** P  < 0.01; *** P  < 0.001)

Histomorphometric analysis

No histopathologic lesions were detected in all groups of tilapia-fed basal diets or diets supplemented with SeNPs at either dose (Fig.  5 ). Likewise, the number of mucins-producing GCs significantly increased from 28.78 ± 0.83 in control fish group to 44.78 ± 0.97 and 54.56 ± 0.53 ( P  < 0.05) in the SeNPs 0.75 &1.5 fish groups, respectively. In contrast, mucin-free GCs significantly decreased ( P  < 0.05) to 14.11 ± 0.6 in the intestines of the SeNPs 1.5 fish group, and 16.11 ± 0.78 in the SeNPs 0.75 fish group from 29.11 ± 0.93 of the control un-supplemented fish. Among GCs, acid mucin-producing GCs significantly increased (39.56 ± 0.53, P  < 0.05) in the fish fed on SeNPs 1.5 fish group, followed by the SeNPs 0.75 fish group (27.89 ± 0.78), as compared with the control un-supplemented fish (20.56 ± 0.88), while the number of neutral mucin-producing GCs decreased significantly (5.78 ± 0.83, P  < 0.05) in the intestines of the SeNPs 1.5 fish group, followed by the SeNPs 1.5 fish group (7.78 ± 0.83) compared to the control (9.44 ± 0.53). However, SeNPs supplementation did not significantly affect the number of mixed mucin-producing GCs ( P  > 0.19) (Fig.  6 A & B ).

Photomicrographs of H&E-counter stained transverse sections from the intestine of non-supplemented Nile tilapia or supplemented with biosynthesized selenium nanoparticle (SeNPs) at SeNPs 0.75  mg/Kg or SeNPs 1.5  mg/Kg showing no structural damage. Low magnification (X10, bar 100 µm). Control = group fed basal diet; SeNPs 0.75  = group fed basal diet with the addition of 0.75 mg/kg SeNPs; and SeNPs 1.5  = group fed basal diet with the addition of 1.5 mg/kg SeNPs

Differential count of the goblet cells (GCs) in the intestine of Nile tilapia fed on SeNPs 0.75  mg/Kg, or SeNPs 1.5  mg/Kg feed or basal diets for 8 weeks. A . AB & PAS double staining showing color differentiation of four types of the GCs, including mucin-free (negative stain), acid mucin-producing (blue, A), neutral mucin-producing (pink, N), and mixed mucin-producing cells (purple, M). B Bars demonstrate the statistical analysis of the intestinal goblet cells count of non-supplemented Nile tilapia or fed with SeNPs 0.75  mg/Kg, or SeNPs 1.5  mg/Kg supplemented diets. Data were expressed as Mean ± SEM. Values with a different letter superscript are significantly different between groups

Selenium nanoparticles have small particle size and large surface characteristics, which potentiate higher permeability and availability in the body of fish [ 6 ]. TEM analysis confirmed the green biosynthesis of SeNPs using P. boryanum , which could be used for the biological reduction and stabilization of selenium metal ions due to phenolic compounds found in this microalga: gallic, protocatechuic, chlorogenic, hydroxybenzoic, and vanillic [ 49 ], which have participated as biological reducing agents in salted ions and converted into nanoparticles, subsequently stabilizing these particles are marked by zeta potential values [ 5 , 6 , 50 ]. Thus, the green route for biosynthesis of nano-selenium from algal extracts is an economically viable mechanism that contributes to stable selenium nanoparticle formation [ 51 ]. The major site of fish digestion and immunity is the intestinal tract, which elucidates absorption and health status, as the intestine is widely related to the teleost intestinal immune barrier [ 52 ]. In the present study, little information was available on the effects of dietary microalgae derived SeNPs supplementation on the physiological, intestinal immune, and antioxidant capacities correlated with the histological parameters of Nile tilapia.

Se supplementation in a fish diet is essential to evaluate the optimum requirements for dietary Se levels to maintain the health status and subsequent a better stress resistance capability of fish [ 53 ]. In addition, commercial diets of cultured fish may not satisfy their demand for selenium because of the low availability of Se from fishmeal diets, as well as the effects of various environmental stressors in reared water [ 54 ]. In the present study, dietary inclusion of two concentrations of SeNPs (0.75 and 1.5 mg/kg) was provided to Nile tilapia to investigate the assimilation of Se in the muscle tissue and the overall effects on immune response in comparison with the normal inorganic Se source present in the mineral premix of the basal diets. In general, many studies have documented Se requirements of many fish species ranging from 0.2 to 12 mg/kg, which could be related to physiological changes in fish, Se concentrations in cultured water, time of exposure, and Se sources (organic, inorganic, or nano form) [ 53 , 55 , 56 ]. For Nile tilapia, dietary optimum levels of seleno-methionine were determined at 1.06–2.06 mg/kg diet for 10 weeks, having a beneficial effect on the tissue bioavailability and antioxidant enzymes activity, whereas higher dietary Se levels between 6.31–14.7 mg /kg diet revealed selenium toxicity via impairment in most of the physiological indices and retarded growth [ 57 ]. Furthermore, a previous study evaluated the optimum dietary Se requirement of tilapia at 1.23 mg nano-Se/kg feed for 90 days to enhance growth and expression of immune-related genes [ 58 ]. The Se concentrations of this study are in favor of those reports, and the optimum Se concentration was close to 1.0–2.0 mg/kg of Se, which was informed to provide a beneficial impact on tilapia and to avoid potential harmful outcomes from higher inclusion Se level in Nile tilapia.

Se levels in different fish tissues have been shown to be remarkable indices for evaluating the status and bioavailability of Se [ 57 ]. In particular, bioaccumulation of Se in fish fillets is important because of its prospective influence on consumers [ 59 ]. Likewise, it has been emphasized that Se supplementation in nanoparticle form in fish diets is more bioavailable and well-assimilated by fish than other sources of Se [ 58 , 60 ]. According to our observations, Se concentrations in the musculature of Nile tilapia notably increased in a dose-dependent manner. Higher musculature Se concentrations with increasing dietary Se levels have been assayed in a variety of fish species [ 60 , 61 , 62 ]. Similarly, the musculature Se content of Nile tilapia fed 0.30 mg/kg Se for 10 weeks significantly increased [ 57 ]. Se content in the muscle tissue of Nile tilapia was significantly increased proportionally by dietary nano-Se supplementation (0.5, 0.1, 0.2 mg/kg) for 90 days [ 58 ]. These findings suggest that fish dietary nano-Se is efficient absorbed and bioavailability, that underscores the benefits of utilizing a Se-derived product to enhance Se levels.

Serum enzyme activities provide a critical evaluation of the health status of liver damage and cellular membranes of aquatic species [ 63 ]. Therefore, changes in serum biochemical parameters are frequently the first measurable indicators of ecological stress [ 64 ]. Se plays a vital role in regulating hepatic functions in the detoxification process, and biochemical indices are markedly influenced by a nutritionally balanced aquafeed and its content [ 65 ]. The present study showed a significant decrease in the serum levels of ALP in SeNPs 0.75 and SeNPs 1.5 , while LDH was significantly reduced in SeNPs 1.5 , compared to the control group. Numerous studies have reported the effects of SeNPs on serum enzyme activities in various species. The first report indicated a marked reduction in serum AST, ALT, and ALP levels in Nile tilapias supplemented with a 0.7 mg/kg SeNPs diet for 9 weeks [ 8 ]. In addition, there was no significant difference in ALP serum activity among experimental common carp fed 0.5, 1, or 2 mg nano-Se/kg diets for 8 weeks [ 61 ]. However, this enzyme was markedly reduced in Nile tilapia fed 0.5, 1, and 2 mg nano-Se/kg diets for 90 d [ 66 ]. Our results are also similar to a previous report on common carp fed nano-selenium (0.7 mg/kg for 8 weeks, showing the lowest values of LDH compared with the control [ 67 ]. These results suggest that the fish were not stressed with the supplemented SeNPs doses and imposed no devastating effects on their hepatic health status.

As observed, cholesterol levels were significantly decreased in both SeNPs doses, while higher HDL levels were observed only in SeNPs 1.5 fish group. These findings confirmed the potential role in regulation of lipid metabolism, where Se as antioxidant agent diminish the ROS production, which is required for the adipocyte-differentiation markers such as peroxisome proliferator-activated receptor (PPARγ), and thus disrupting with lipid deposition without cytotoxic effects [ 68 ]. Our findings were consistent with previous studies used nano-Se supplementation, like in Common carp fed on 2 and 0.7 mg nano-Se/kg for 8 weeks [ 9 , 61 ], grass carp fed on at 0.3 mg/kg and 1.2 mg/kg [ 69 ], and Asian seabass ( Lates calcarifer ) fed Se on 4 mg/kg for four weeks [ 70 ]. On contrary, no significant differences were observed in total cholesterol and TG serum levels in Nile tilapia fed dietary chemically synthesized SeNPs (1 mg/kg) for two months [ 71 ]. These discrepancies could be related to different fish age, SeNPs dosage and synthesis method.

In the current study, upregulation of IL-1β and IL-8 were observed, suggesting a better immune response after high-dose SeNPs supplementation, with no evidence of inflammatory changes as reflected by the mRNA levels of TNF-α and TGF-β1 , coupled with normal histological intestinal morphometry in the present study. This finding implies that SeNPs potentially exert an immunomodulatory effect, that is indirectly related to its antioxidant activity reflected by upregulation of antioxidant-related genes expression. The glutathione family is required for strengthening the immune functions, including the proliferation of cells and activation of T cells and polymorphonuclear leukocytes in vivo [ 72 ]. In addition, As Se decreases ROS production, it inhibits the NF k β cascade [ 72 , 73 ], with subsequent suppression of pro-inflammatory cytokines like TNF-α and TGF-β1 as shown herein. In accordance with these findings, increased IL-1β and IL-8 expression was observed after dietary supplementation of selenium-loaded chitosan nanoparticles (SeChNPs) in the liver and spleen of Nile Tilapia ( Oreochromis niloticus ) in a dose-dependent manner (0.5, 1, and 2 g/kg) [ 74 ]. Consistent with our findings, H Jingyuan, L Yan, P Wenjing, J Wenqiang, L Bo, M Linghong, Z Qunlang, L Hualiang and G Xianping [ 75 ] observed no significant alterations in the mRNA levels of TGF-β1, and TNF-α after dietary supplementation of different levels of selenium (0.10, 0.42, 0.67, 1.06 and 1.46 mg Se/kg) in juvenile blunt snout bream.

The expression of the antioxidant GST , GSS , GSR , and GPx genes was upregulated in the intestine of Nile tilapia-supplemented biosynthesized Se nanoparticles. Many reports have also positively elucidated the effect of SeNPs in enhancing the capacity of antioxidative enzymes (SOD, CAT, and GPx) in grass carp ( Ctenopharyngodon idella ) [ 69 ], Asian seabass [ 54 ], Nile tilapia [ 65 , 76 ], common carp [ 9 , 61 ], and European seabass ( Dicentrarchus labrax ) [ 77 ]. More specifically, Se nanoparticles can reinforce the intestinal antioxidant capacity, as elemental Se plays a pivotal role in building selenoproteins, functional components of GSH, and GPx enzymes, which prohibit cellular membrane peroxidation by catalyzing the removal of reactive oxygen species (ROS) in the fish body [ 11 , 63 , 78 ]. GPx-containing selenol is oxidized by H 2 O 2 or other oxidants, which generates selenenic acid (GPx-SeOH). Subsequently, GPx-SeOH is converted into selenol. Subsequently, selenenyl sulfide (GPx-SeSG) is produced by the reaction between GPx-SeOH and GSH, which reduces GPx-SeSG to selenol [ 63 , 79 ]. Additionally, nanoparticle forms of Se have been shown to promote GPx gene expression through the formation of selenophosphate [ 80 ]. Therefore, these enzymes have been noted as indicators of the effects of selenium on antioxidant mechanisms in fish [ 67 ]. Besides, the P. boryanum extracts showed the highest radical scavenging activity among Chloromonas cf. reticulata and Chloroidium saccharophilum microalgae due to the presence of Catechin, epicatechin, gallic acid, and vanillic phenolic compounds in P. boryanum microalga as natural antioxidants, neutralizing the reactive species of oxygen and nitrogen, subsequently prohibiting the lipid oxidative damage [ 81 , 82 ]. Microalgal P. boryanum derived polysaccharides contribute to the modulation of antioxidant function regulation and increasing immunity response [ 49 , 72 ].

As noted in the current study, the intestinal transcriptional levels of PCNA and caspase -3 in Nile tilapia remained unchanged after eight weeks of supplementation with biosynthesized SeNPs. Our findings are consistent with the protective effect of supplemented manganese nanoparticles (Mn-NPs) evidenced by the downregulation of caspase gene expression in Pangasianodon hypophthalmus fish [ 83 ], and in Aeromonas -challenged Nile tilapia dietary Se-loaded chitosan nanoparticles (0.5 g/kg), compared with control group [ 74 ]. The obtained data suggest a key role of Se in sustaining intestinal epithelial proliferation without apoptotic modifications, as previously documented [ 84 ], highlighting the role of SeNPs as powerful antioxidant agents, eliminating reactive oxygen species (ROS), which is linked to mitochondria-mediated apoptosis, caspase -3 activation, and cleavage of poly (ADP-ribose) polymerase-1 ( PARP ) [ 85 ].

Our investigations revealed a significant increase in the number of mucin-producing GCs in the intestine of Nile tilapia fed SeNPs-supplemented diets (SeNPs 1.5 or SeNPs 0.75 ) compared to that in control non-supplemented fish. The secreted intestinal mucin is made of glycoproteins and contains a number of bioactive molecules [ 86 ]. Intestinal mucin-filled GCs indicate mucin production, a potential component of the intestinal innate gut immune system [ 87 ]. In this study, a significant higher number of GCs producing acid mucins, which are sulfated intestinal mucins, was observed with no changes GCs producing mixed or neutral mucins. Sulfated mucins are resistant to lysis by host proteases and bacterial glycosidases thus conferring protection to the intestinal mucosa [ 47 , 88 ], while neutral and mixed mucins participate in lubrication and osmoregulation [ 89 ]. Our results were consistent with those reported by S Ghaniem, E Nassef, AI Zaineldin, A Bakr and S Hegazi [ 90 ], who reported an increasing number of GCs in the anterior and posterior intestines of Nile tilapia fed SeNPs-supplemented diets (1 mg/kg diet) for 65 days. Upon integrating these findings with the intestinal cytokine and antioxidant-related gene expression, it is possible to deduce that SeNPs, as effective antioxidant agents, possess the capacity to mitigate intestinal inflammation and reduce the production of intestinal ROS. These effects are indirectly associated with the development of goblet cells and the promotion of mucus layer formation, which protects intestinal tissues [ 88 , 91 ].

Conclusions

In the current study, biochemical indices, Se bioavailability, expression patterns of intestinal antioxidant-related genes, IL-8 and IL1β immune regulating genes, and goblet cell proliferation were enhanced by the incorporation of SeNPs in Nile tilapia diet, particularly at dose of 1.5 mg/kg diet. Further, SeNPs supplementation did not induce any damage as indicated by levels of PCNA and apoptotic genes expression. Therefore, incorporation of biogenic SeNPs into aquafeeds could potentially improve Nile tilapia immunity and sustainability.

Availability of data and materials

All data supporting the findings of this study are available within the paper.

Abbreviations

Association of official analytical chemists

Distilled water

Glutathione peroxidase

Glutathione reductase

Glutathione synthetase

Glutathione-S-transferase

Goblet cells

High-density lipoproteins

Interleukin-1β

Interleukin 8

Condition factor

Lactate dehydrogenase

Low-density lipoproteins

Tricaine methanesulfonate

• Nanoparticles

Nuclear transcription factor-κB

National Research Council

Pediastrum boryanum

Reactive oxygen species

Selenium nanoparticles

Serum alkaline phosphatase

Specific growth rate

Triglycerides

Transforming growth factor-β1

Tumor necrosis factor- α

Transmission electron microscopy

X-ray diffraction

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The authors would like to thank the organizations and individuals who provided in-kind support for this study.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This research received no specific grants from any funding agency in the public, commercial, or not-for-profit sectors.

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Department of Aquatic Animal Medicine, Faculty of Veterinary Medicine, Mansoura University, Mansoura, 35516, Egypt

Eman Zahran & Samia Elbahnaswy

Department of Zoology, Faculty of Science, Sohag University, Sohag, 82524, Egypt

Fatma Ahmed

Department of Clinical Pathology, Faculty of Veterinary Medicine, Mansoura University, Mansoura, 35516, Egypt

Animal and Fish Production Department, College of Agricultural and Food Sciences, King Faisal University, P.O. Box 420, Al-Ahsa, 31982, Saudi Arabia

Abdallah Tageldein Mansour

Fish and Animal Production Department, Faculty of Agriculture (Saba Basha), Alexandria University, Alexandria, 21531, Egypt

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Arwa sultan Alqahtani

Department of Pathology, Faculty of Veterinary Medicine, Mansoura University, Mansoura, 35516, Egypt

Walaa Awadin

Department of Biomedical Sciences, College of Veterinary Medicine, King Faisal University, Al-Ahsa, 31982, Saudi Arabia

Mahmoud G. El Sebaei

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E.Z: conceptualization, investigation, methodology, formal analysis, validation, review, editing, and correspondence. S.E.: Methodology, investigation, and writing of the original draft. F.A: Histopathological examination, investigation, and contributed to writing the original draft. E.R., A.T.M., A.S.A., and M.G.E.: The investigation, and resources. W.A.: Histopathological analysis. All authors have read and approved the final manuscript.

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TacticAI: an AI assistant for football tactics

• Zhe Wang   ORCID: orcid.org/0000-0002-0748-5376 1   na1 ,
• Petar Veličković   ORCID: orcid.org/0000-0002-2820-4692 1   na1 ,
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• Nenad Tomašev   ORCID: orcid.org/0000-0003-1624-0220 1 ,
• Laurel Prince 1 ,
• Michael Kaisers 1 ,
• Yoram Bachrach 1 ,
• Romuald Elie 1 ,
• Li Kevin Wenliang 1 ,
• Federico Piccinini 1 ,
• William Spearman 2 ,
• Ian Graham 3 ,
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• Yi Yang 1 ,
• Adrià Recasens 1 ,
• Mina Khan 1 ,
• Nathalie Beauguerlange 1 ,
• Pablo Sprechmann 1 ,
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• Nicolas Heess   ORCID: orcid.org/0000-0001-7876-9256 1 ,
• Michael Bowling   ORCID: orcid.org/0000-0003-2960-8418 4 ,
• Demis Hassabis 1 &
• Karl Tuyls   ORCID: orcid.org/0000-0001-7929-1944 5  

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Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI’s model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.

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Introduction

Association football, or simply football or soccer, is a widely popular and highly professionalised sport, in which two teams compete to score goals against each other. As each football team comprises up to 11 active players at all times and takes place on a very large pitch (also known as a soccer field), scoring goals tends to require a significant degree of strategic team-play. Under the rules codified in the Laws of the Game 1 , this competition has nurtured an evolution of nuanced strategies and tactics, culminating in modern professional football leagues. In today’s play, data-driven insights are a key driver in determining the optimal player setups for each game and developing counter-tactics to maximise the chances of success 2 .

When competing at the highest level the margins are incredibly tight, and it is increasingly important to be able to capitalise on any opportunity for creating an advantage on the pitch. To that end, top-tier clubs employ diverse teams of coaches, analysts and experts, tasked with studying and devising (counter-)tactics before each game. Several recent methods attempt to improve tactical coaching and player decision-making through artificial intelligence (AI) tools, using a wide variety of data types from videos to tracking sensors and applying diverse algorithms ranging from simple logistic regression to elaborate neural network architectures. Such methods have been employed to help predict shot events from videos 3 , forecast off-screen movement from spatio-temporal data 4 , determine whether a match is in-play or interrupted 5 , or identify player actions 6 .

The execution of agreed-upon plans by players on the pitch is highly dynamic and imperfect, depending on numerous factors including player fitness and fatigue, variations in player movement and positioning, weather, the state of the pitch, and the reaction of the opposing team. In contrast, set pieces provide an opportunity to exert more control on the outcome, as the brief interruption in play allows the players to reposition according to one of the practiced and pre-agreed patterns, and make a deliberate attempt towards the goal. Examples of such set pieces include free kicks, corner kicks, goal kicks, throw-ins, and penalties 2 .

Among set pieces, corner kicks are of particular importance, as an improvement in corner kick execution may substantially modify game outcomes, and they lend themselves to principled, tactical and detailed analysis. This is because corner kicks tend to occur frequently in football matches (with ~10 corners on average taking place in each match 7 ), they are taken from a fixed, rigid position, and they offer an immediate opportunity for scoring a goal—no other set piece simultaneously satisfies all of the above. In practice, corner kick routines are determined well ahead of each match, taking into account the strengths and weaknesses of the opposing team and their typical tactical deployment. It is for this reason that we focus on corner kick analysis in particular, and propose TacticAI, an AI football assistant for supporting the human expert with set piece analysis, and the development and improvement of corner kick routines.

TacticAI is rooted in learning efficient representations of corner kick tactics from raw, spatio-temporal player tracking data. It makes efficient use of this data by representing each corner kick situation as a graph—a natural representation for modelling relationships between players (Fig.  1 A, Table  2 ), and these player relationships may be of higher importance than the absolute distances between them on the pitch 8 . Such a graph input is a natural candidate for graph machine learning models 9 , which we employ within TacticAI to obtain high-dimensional latent player representations. In the Supplementary Discussion section, we carefully contrast TacticAI against prior art in the area.

A How corner kick situations are converted to a graph representation. Each player is treated as a node in a graph, with node, edge and graph features extracted as detailed in the main text. Then, a graph neural network operates over this graph by performing message passing; each node’s representation is updated using the messages sent to it from its neighbouring nodes. B How TacticAI processes a given corner kick. To ensure that TacticAI’s answers are robust in the face of horizontal or vertical reflections, all possible combinations of reflections are applied to the input corner, and these four views are then fed to the core TacticAI model, where they are able to interact with each other to compute the final player representations—each internal blue arrow corresponds to a single message passing layer from ( A ). Once player representations are computed, they can be used to predict the corner’s receiver, whether a shot has been taken, as well as assistive adjustments to player positions and velocities, which increase or decrease the probability of a shot being taken.

Uniquely, TacticAI takes advantage of geometric deep learning 10 to explicitly produce player representations that respect several symmetries of the football pitch (Fig.  1 B). As an illustrative example, we can usually safely assume that under a horizontal or vertical reflection of the pitch state, the game situation is equivalent. Geometric deep learning ensures that TacticAI’s player representations will be identically computed under such reflections, such that this symmetry does not have to be learnt from data. This proves to be a valuable addition, as high-quality tracking data is often limited—with only a few hundred matches played each year in every league. We provide an in-depth overview of how we employ geometric deep learning in TacticAI in the “Methods” section.

From these representations, TacticAI is then able to answer various predictive questions about the outcomes of a corner—for example, which player is most likely to make first contact with the ball, or whether a shot will take place. TacticAI can also be used as a retrieval system—for mining similar corner kick situations based on the similarity of player representations—and a generative recommendation system, suggesting adjustments to player positions and velocities to maximise or minimise the estimated shot probability. Through several experiments within a case study with domain expert coaches and analysts from Liverpool FC, the results of which we present in the next section, we obtain clear statistical evidence that TacticAI readily provides useful, realistic and accurate tactical suggestions.

To demonstrate the diverse qualities of our approach, we design TacticAI with three distinct predictive and generative components: receiver prediction, shot prediction, and tactic recommendation through guided generation, which also correspond to the benchmark tasks for quantitatively evaluating TacticAI. In addition to providing accurate quantitative insights for corner kick analysis with its predictive components, the interplay between TacticAI’s predictive and generative components allows coaches to sample alternative player setups for each routine of interest, and directly evaluate the possible outcomes of such alternatives.

We will first describe our quantitative analysis, which demonstrates that TacticAI’s predictive components are accurate at predicting corner kick receivers and shot situations on held-out test corners and that the proposed player adjustments do not strongly deviate from ground-truth situations. However, such an analysis only gives an indirect insight into how useful TacticAI would be once deployed. We tackle this question of utility head-on and conduct a comprehensive case study in collaboration with our partners at Liverpool FC—where we directly ask human expert raters to judge the utility of TacticAI’s predictions and player adjustments. The following sections expand on the specific results and analysis we have performed.

In what follows, we will describe TacticAI’s components at a minimal level necessary to understand our evaluation. We defer detailed descriptions of TacticAI’s components to the “Methods” section. Note that, all our error bars reported in this research are standard deviations.

Benchmarking TacticAI

We evaluate the three components of TacticAI on a relevant benchmark dataset of corner kicks. Our dataset consists of 7176 corner kicks from the 2020 to 2021 Premier League seasons, which we randomly shuffle and split into a training (80%) and a test set (20%). As previously mentioned, TacticAI operates on graphs. Accordingly, we represent each corner kick situation as a graph, where each node corresponds to a player. The features associated with each node encode the movements (velocities and positions) and simple profiles (heights and weights) of on-pitch players at the timestamp when the corresponding corner kick was being taken by the attacking kicker (see the “Methods” section), and no information of ball movement was encoded. The graphs are fully connected; that is, for every pair of players, we will include the edge connecting them in the graph. Each of these edges encodes a binary feature, indicating whether the two players are on opposing teams or not. For each task, we generated the relevant dataset of node/edge/graph features and corresponding labels (Tables  1 and 2 , see the “Methods” section). The components were then trained separately with their corresponding corner kick graphs. In particular, we only employ a minimal set of features to construct the corner kick graphs, without encoding the movements of the ball nor explicitly encoding the distances between players into the graphs. We used a consistent training-test split for all benchmark tasks, as this made it possible to benchmark not only the individual components but also their interactions.

Accurate receiver and shot prediction through geometric deep learning

One of TacticAI’s key predictive models forecasts the receiver out of the 22 on-pitch players. The receiver is defined as the first player touching the ball after the corner is taken. In our evaluation, all methods used the same set of features (see the “Receiver prediction” entry in Table  1 and the “Methods” section). We leveraged the receiver prediction task to benchmark several different TacticAI base models. Our best-performing model—achieving 0.782 ± 0.039 in top-3 test accuracy after 50,000 training steps—was a deep graph attention network 11 , 12 , leveraging geometric deep learning 10 through the use of D 2 group convolutions 13 . We supplement this result with a detailed ablation study, verifying that both our choice of base architecture and group convolution yielded significant improvements in the receiver prediction task (Supplementary Table  2 , see the subsection “Ablation study” in the “Methods” section). Considering that corner kick receiver prediction is a highly challenging task with many factors that are unseen by our model—including fatigue and fitness levels, and actual ball trajectory—we consider TacticAI’s top-3 accuracy to reflect a high level of predictive power, and keep the base TacticAI architecture fixed for subsequent studies. In addition to this quantitative evaluation with the evaluation dataset, we also evaluate the performance of TacticAI’s receiver prediction component in a case study with human raters. Please see the “Case study with expert raters” section for more details.

For shot prediction, we observe that reusing the base TacticAI architecture to directly predict shot events—i.e., directly modelling the probability \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{corner}}}\,)\) —proved challenging, only yielding a test F 1 score of 0.52 ± 0.03, for a GATv2 base model. Note that here we use the F 1 score—the harmonic mean of precision and recall—as it is commonly used in binary classification problems over imbalanced datasets, such as shot prediction. However, given that we already have a potent receiver predictor, we decided to use its output to give us additional insight into whether or not a shot had been taken. Hence, we opted to decompose the probability of taking a shot as

where \({\mathbb{P}}(\,{{\mbox{receiver}}}| {{\mbox{corner}}}\,)\) are the probabilities computed by TacticAI’s receiver prediction system, and \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{receiver}}},{{\mbox{corner}}}\,)\) models the conditional shot probability after a specific player makes first contact with the ball. This was implemented through providing an additional global feature to indicate the receiver in the corresponding corner kick (Table  1 ) while the architecture otherwise remained the same as that of receiver prediction (Supplementary Fig.  2 , see the “Methods” section). At training time, we feed the ground-truth receiver as input to the model—at inference time, we attempt every possible receiver, weighing their contributions using the probabilities given by TacticAI’s receiver predictor, as per Eq. ( 1 ). This two-phased approach yielded a final test F 1 score of 0.68 ± 0.04 for shot prediction, which encodes significantly more signal than the unconditional shot predictor, especially considering the many unobservables associated with predicting shot events. Just as for receiver prediction, this performance can be further improved using geometric deep learning; a conditional GATv2 shot predictor with D 2 group convolutions achieves an F 1 score of 0.71 ± 0.01.

Moreover, we also observe that, even just through predicting the receivers, without explicitly classifying any other salient features of corners, TacticAI learned generalisable representations of the data. Specifically, team setups with similar tactical patterns tend to cluster together in TacticAI’s latent space (Fig.  2 ). However, no clear clusters are observed in the raw input space (Supplementary Fig.  1 ). This indicates that TacticAI can be leveraged as a useful corner kick retrieval system, and we will present our evaluation of this hypothesis in the “Case study with expert raters” section.

We visualise the latent representations of attacking and defending teams in 1024 corner kicks using t -SNE. A latent team embedding in one corner kick sample is the mean of the latent player representations on the same attacking ( A – C ) or defending ( D ) team. Given the reference corner kick sample ( A ), we retrieve another corner kick sample ( B ) with respect to the closest distance of their representations in the latent space. We observe that ( A ) and ( B ) are both out-swing corner kicks and share similar patterns of their attacking tactics, which are highlighted with rectangles having the same colours, although they bear differences with respect to the absolute positions and velocities of the players. All the while, the latent representation of an in-swing attack ( C ) is distant from both ( A ) and ( B ) in the latent space. The red arrows are only used to demonstrate the difference between in- and out-swing corner kicks, not the actual ball trajectories.

Lastly, it is worth emphasising that the utility of the shot predictor likely does not come from forecasting whether a shot event will occur—a challenging problem with many imponderables—but from analysing the difference in predicted shot probability across multiple corners. Indeed, in the following section, we will show how TacticAI’s generative tactic refinements can directly influence the predicted shot probabilities, which will then corresponds to highly favourable evaluation by our expert raters in the “Case study with expert raters” section.

Controlled tactic refinement using class-conditional generative models

Equipped with components that are able to potently relate corner kicks with their various outcomes (e.g. receivers and shot events), we can explore the use of TacticAI to suggest adjustments of tactics, in order to amplify or reduce the likelihood of certain outcomes.

Specifically, we aim to produce adjustments to the movements of players on one of the two teams, including their positions and velocities, which would maximise or minimise the probability of a shot event, conditioned on the initial corner setup, consisting of the movements of players on both teams and their heights and weights. In particular, although in real-world scenarios both teams may react simultaneously to the movements of each other, in our study, we focus on moderate adjustments to player movements, which help to detect players that are not responding to a tactic properly. Due to this reason, we simplify the process of tactic refinement through generating the adjustments for only one team while keeping the other fixed. The way we train a model for this task is through an auto-encoding objective: we feed the ground-truth shot outcome (a binary indicator) as an additional graph-level feature to TacticAI’s model (Table  1 ), and then have it learn to reconstruct a probability distribution of the input player coordinates (Fig.  1 B, also see the “Methods” section). As a consequence, our tactic adjustment system does not depend on the previously discussed shot predictor—although we can use the shot predictor to evaluate whether the adjustments make a measurable difference in shot probability.

This autoencoder-based generative model is an individual component that separates from TacticAI’s predictive systems. All three systems share the encoder architecture (without sharing parameters), but use different decoders (see the “Methods” section). At inference time, we can instead feed in a desired shot outcome for the given corner setup, and then sample new positions and velocities for players on one team using this probability distribution. This setup, in principle, allows for flexible downstream use, as human coaches can optimise corner kick setups through generating adjustments conditioned on the specific outcomes of their interest—e.g., increasing shot probability for the attacking team, decreasing it for the defending team (Fig.  3 ) or amplifying the chance that a particular striker receives the ball.

TacticAI makes it possible for human coaches to redesign corner kick tactics in ways that help maximise the probability of a positive outcome for either the attacking or the defending team by identifying key players, as well as by providing temporally coordinated tactic recommendations that take all players into consideration. As demonstrated in the present example ( A ), for a corner kick in which there was a shot attempt in reality ( B ), TacticAI can generate a tactically-adjusted setting in which the shot probability has been reduced, by adjusting the positioning of the defenders ( D ). The suggested defender positions result in reduced receiver probability for attacking players 2–5 (see bottom row), while the receiver probability of Attacker 1, who is distant from the goalpost, has been increased ( C ). The model is capable of generating multiple such scenarios. Coaches can inspect the different options visually and additionally consult TacticAI’s quantitative analysis of the presented tactics.

We first evaluate the generated adjustments quantitatively, by verifying that they are indistinguishable from the original corner kick distribution using a classifier. To do this, we synthesised a dataset consisting of 200 corner kick samples and their corresponding conditionally generated adjustments. Specifically, for corners without a shot event, we generated adjustments for the attacking team by setting the shot event feature to 1, and vice-versa for the defending team when a shot event did happen. We found that the real and generated samples were not distinguishable by an MLP classifier, with an F 1 score of 0.53 ± 0.05, indicating random chance level accuracy. This result indicates that the adjustments produced by TacticAI are likely similar enough to real corner kicks that the MLP is unable to tell them apart. Note that, in spite of this similarity, TacticAI recommends player-level adjustments that are not negligible—in the following section we will illustrate several salient examples of this. To more realistically validate the practical indistinguishability of TacticAI’s adjustments from realistic corners, we also evaluated the realism of the adjustments in a case study with human experts, which we will present in the following section.

In addition, we leveraged our TacticAI shot predictor to estimate whether the proposed adjustments were effective. We did this by analysing 100 corner kick samples in which threatening shots occurred, and then, for each sample, generated one defensive refinement through setting the shot event feature to 0. We observed that the average shot probability significantly decreased, from 0.75 ± 0.14 for ground-truth corners to 0.69 ± 0.16 for adjustments ( z  = 2.62,  p  < 0.001). This observation was consistent when testing for attacking team refinements (shot probability increased from 0.18 ± 0.16 to 0.31 ± 0.26 ( z  = −4.46,  p  < 0.001)). Moving beyond this result, we also asked human raters to assess the utility of TacticAI’s proposed adjustments within our case study, which we detail next.

Case study with expert raters

Although quantitative evaluation with well-defined benchmark datasets was critical for the technical development of TacticAI, the ultimate test of TacticAI as a football tactic assistant is its practical downstream utility being recognised by professionals in the industry. To this end, we evaluated TacticAI through a case study with our partners at Liverpool FC (LFC). Specifically, we invited a group of five football experts: three data scientists, one video analyst, and one coaching assistant. Each of them completed four tasks in the case study, which evaluated the utility of TacticAI’s components from several perspectives; these include (1) the realism of TacticAI’s generated adjustments, (2) the plausibility of TacticAI’s receiver predictions, (3) effectiveness of TacticAI’s embeddings for retrieving similar corners, and (4) usefulness of TacticAI’s recommended adjustments. We provide an overview of our study’s results here and refer the interested reader to Supplementary Figs.  3 – 5 and the  Supplementary Methods for additional details.

We first simultaneously evaluated the realism of the adjusted corner kicks generated by TacticAI, and the plausibility of its receiver predictions. Going through a collection of 50 corner kick samples, we first asked the raters to classify whether a given sample was real or generated by TacticAI, and then they were asked to identify the most likely receivers in the corner kick sample (Supplementary Fig.  3 ).

On the task of classifying real and generated samples, first, we found that the raters’ average F 1 score of classifying the real vs. generated samples was only 0.60 ± 0.04, with individual F 1 scores ( \({F}_{1}^{A}=0.54,{F}_{1}^{B}=0.64,{F}_{1}^{C}=0.65,{F}_{1}^{D}=0.62,{F}_{1}^{E}=0.56\) ), indicating that the raters were, in many situations, unable to distinguish TacticAI’s adjustments from real corners.

The previous evaluation focused on analysing realism detection performance across raters. We also conduct a study that analyses realism detection across samples. Specifically, we assigned ratings for each sample—assigning +1 to a sample if it was identified as real by a human rater, and 0 otherwise—and computed the average rating for each sample across the five raters. Importantly, by studying the distribution of ratings, we found that there was no significant difference between the average ratings assigned to real and generated corners ( z  = −0.34,  p  > 0.05) (Fig.  4 A). Hence, the real and generated samples were assigned statistically indistinguishable average ratings by human raters.

In task 1, we tested the statistical difference between the real corner kick samples and the synthetic ones generated by TacticAI from two aspects: ( A.1 ) the distributions of their assigned ratings, and ( A.2 ) the corresponding histograms of the rating values. Analogously, in task 2 (receiver prediction), ( B.1 ) we track the distributions of the top-3 accuracy of receiver prediction using those samples, and ( B.2 ) the corresponding histogram of the mean rating per sample. No statistical difference in the mean was observed in either cases (( A.1 ) ( z  = −0.34,  p  > 0.05), and ( B.1 ) ( z  = 0.97,  p  > 0.05)). Additionally, we observed a statistically significant difference between the ratings of different raters on receiver prediction, with three clear clusters emerging ( C ). Specifically, Raters A and E had similar ratings ( z  = 0.66,  p  > 0.05), and Raters B and D also rated in similar ways ( z  = −1.84,  p  > 0.05), while Rater C responded differently from all other raters. This suggests a good level of variety of the human raters with respect to their perceptions of corner kicks. In task 3—identifying similar corners retrieved in terms of salient strategic setups—there were no significant differences among the distributions of the ratings by different raters ( D ), suggesting a high level of agreement on the usefulness of TacticAI’s capability of retrieving similar corners ( F 1,4  = 1.01,  p  > 0.1). Finally, in task 4, we compared the ratings of TacticAI’s strategic refinements across the human raters ( E ) and found that the raters also agreed on the general effectiveness of the refinements recommended by TacticAI ( F 1,4  = 0.45,  p  > 0.05). Note that the violin plots used in B.1 and C – E model a continuous probability distribution and hence assign nonzero probabilities to values outside of the allowed ranges. We only label y -axis ticks for the possible set of ratings.

For the task of identifying receivers, we rated TacticAI’s predictions with respect to a rater as +1 if at least one of the receivers identified by the rater appeared in TacticAI’s top-3 predictions, and 0 otherwise. The average top-3 accuracy among the human raters was 0.79 ± 0.18; specifically, 0.81 ± 0.17 for the real samples, and 0.77 ± 0.21 for the generated ones. These scores closely line up with the accuracy of TacticAI in predicting receivers for held-out test corners, validating our quantitative study. Further, after averaging the ratings for receiver prediction sample-wise, we found no statistically significant difference between the average ratings of predicting receivers over the real and generated samples ( z  = 0.97,  p  > 0.05) (Fig.  4 B). This indicates that TacticAI was equally performant in predicting the receivers of real corners and TacticAI-generated adjustments, and hence may be leveraged for this purpose even in simulated scenarios.

There is a notably high variance in the average receiver prediction rating of TacticAI. We hypothesise that this is due to the fact that different raters may choose to focus on different salient features when evaluating the likely receivers (or even the amount of likely receivers). We set out to validate this hypothesis by testing the pair-wise similarity of the predictions by the human raters through running a one-away analysis of variance (ANOVA), followed by a Tukey test. We found that the distributions of the five raters’ predictions were significantly different ( F 1,4  = 14.46,  p  < 0.001) forming three clusters (Fig.  4 C). This result indicates that different human raters—as suggested by their various titles at LFC—may often use very different leads when suggesting plausible receivers. The fact that TacticAI manages to retain a high top-3 accuracy in such a setting suggests that it was able to capture the salient patterns of corner kick strategies, which broadly align with human raters’ preferences. We will further test this hypothesis in the third task—identifying similar corners.

For the third task, we asked the human raters to judge 50 pairs of corners for their similarity. Each pair consisted of a reference corner and a retrieved corner, where the retrieved corner was chosen either as the nearest-neighbour of the reference in terms of their TacticAI latent space representations, or—as a feature-level heuristic—the cosine similarities of their raw features (Supplementary Fig.  4 ) in our corner kick dataset. We score the raters’ judgement of a pair as +1 if they considered the corners presented in the case to be usefully similar, otherwise, the pair is scored with 0. We first computed, for each rater, the recall with which they have judged a baseline- or TacticAI-retrieved pair as usefully similar—see description of Task 3 in the  Supplementary Methods . For TacticAI retrievals, the average recall across all raters was 0.59 ± 0.09, and for the baseline system, the recall was 0.36 ± 0.10. Secondly, we assess the statistical difference between the results of the two methods by averaging the ratings for each reference–retrieval pair, finding that the average rating of TacticAI retrievals is significantly higher than the average rating of baseline method retrievals ( z  = 2.34,  p  < 0.05). These two results suggest that TacticAI significantly outperforms the feature-space baseline as a method for mining similar corners. This indicates that TacticAI is able to extract salient features from corners that are not trivial to extract from the input data alone, reinforcing it as a potent tool for discovering opposing team tactics from available data. Finally, we observed that this task exhibited a high level of inter-rater agreement for TacticAI-retrieved pairs ( F 1,4  = 1.01,  p  > 0.1) (Fig.  4 D), suggesting that human raters were largely in agreement with respect to their assessment of TacticAI’s performance.

Finally, we evaluated TacticAI’s player adjustment recommendations for their practical utility. Specifically, each rater was given 50 tactical refinements together with the corresponding real corner kick setups—see Supplementary Fig.  5 , and the “Case study design” section in the  Supplementary Methods . The raters were then asked to rate each refinement as saliently improving the tactics (+1), saliently making them worse (−1), or offering no salient differences (0). We calculated the average rating assigned by each of the raters (giving us a value in the range [− 1, 1] for each rater). The average of these values across all five raters was 0.7 ± 0.1. Further, for 45 of the 50 situations (90%), the human raters found TacticAI’s suggestion to be favourable on average (by majority voting). Both of these results indicate that TacticAI’s recommendations are salient and useful to a downstream football club practitioner, and we set out to validate this with statistical tests.

We performed statistical significance testing of the observed positive ratings. First, for each of the 50 situations, we averaged its ratings across all five raters and then ran a t -test to assess whether the mean rating was significantly larger than zero. Indeed, the statistical test indicated that the tactical adjustments recommended by TacticAI were constructive overall ( \({t}_{49}^{{{{{{{{\rm{avg}}}}}}}}}=9.20,\, p \, < \, 0.001\) ). Secondly, we verified that each of the five raters individually found TacticAI’s recommendations to be constructive, running a t -test on each of their ratings individually. For all of the five raters, their average ratings were found to be above zero with statistical significance ( \({t}_{49}^{A}=5.84,\, {p}^{A} \, < \, 0.001;{t}_{49}^{B}=7.88,\; {p}^{B} \, < \, 0.001;{t}_{49}^{C}=7.00,\; {p}^{C} \, < \, 0.001;{t}_{49}^{D}=6.04,\; {p}^{D} \, < \, 0.001;{t}_{49}^{E}=7.30,\, {p}^{E} \, < \, 0.001\) ). In addition, their ratings also shared a high level of inter-agreement ( F 1,4  = 0.45,  p  > 0.05) (Fig.  4 E), suggesting a level of practical usefulness that is generally recognised by human experts, even though they represent different backgrounds.

Taking all of these results together, we find TacticAI to possess strong components for prediction, retrieval, and tactical adjustments on corner kicks. To illustrate the kinds of salient recommendations by TacticAI, in Fig.  5 we present four examples with a high degree of inter-rater agreement.

These examples are selected from our case study with human experts, to illustrate the breadth of tactical adjustments that TacticAI suggests to teams defending a corner. The density of the yellow circles coincides with the number of times that the corresponding change is recognised as constructive by human experts. Instead of optimising the movement of one specific player, TacticAI can recommend improvements for multiple players in one generation step through suggesting better positions to block the opposing players, or better orientations to track them more efficiently. Some specific comments from expert raters follow. In A , according to raters, TacticAI suggests more favourable positions for several defenders, and improved tracking runs for several others—further, the goalkeeper is positioned more deeply, which is also beneficial. In B , TacticAI suggests that the defenders furthest away from the corner make improved covering runs, which was unanimously deemed useful, with several other defenders also positioned more favourably. In C , TacticAI recommends improved covering runs for a central group of defenders in the penalty box, which was unanimously considered salient by our raters. And in D , TacticAI suggests substantially better tracking runs for two central defenders, along with a better positioning for two other defenders in the goal area.

We have demonstrated an AI assistant for football tactics and provided statistical evidence of its efficacy through a comprehensive case study with expert human raters from Liverpool FC. First, TacticAI is able to accurately predict the first receiver after a corner kick is taken as well as the probability of a shot as the direct result of the corner. Second, TacticAI has been shown to produce plausible tactical variations that improve outcomes in a salient way, while being indistinguishable from real scenarios by domain experts. And finally, the system’s latent player representations are a powerful means to retrieve similar set-piece tactics, allowing coaches to analyse relevant tactics and counter-tactics that have been successful in the past.

The broader scope of strategy modelling in football has previously been addressed from various individual angles, such as pass prediction 14 , 15 , 16 , shot prediction 3 or corner kick tactical classification 7 . However, to the best of our knowledge, our work stands out by combining and evaluating predictive and generative modelling of corner kicks for tactic development. It also stands out in its method of applying geometric deep learning, allowing for efficiently incorporating various symmetries of the football pitch for improved data efficiency. Our method incorporates minimal domain knowledge and does not rely on intricate feature engineering—though its factorised design naturally allows for more intricate feature engineering approaches when such features are available.

Our methodology requires the position and velocity estimates of all players at the time of execution of the corner and subsequent events. Here, we derive these from high-quality tracking and event data, with data availability from tracking providers limited to top leagues. Player tracking based on broadcast video would increase the reach and training data substantially, but would also likely result in noisier model inputs. While the attention mechanism of GATs would allow us to perform introspection of the most salient factors contributing to the model outcome, our method does not explicitly model exogenous (aleatoric) uncertainty, which would be valuable context for the football analyst.

While the empirical study of our method’s efficacy has been focused on corner kicks in association football, it readily generalises to other set pieces (such as throw-ins, which similarly benefit from similarity retrieval, pass and/or shot prediction) and other team sports with suspended play situations. The learned representations and overall framing of TacticAI also lay the ground for future research to integrate a natural language interface that enables domain-grounded conversations with the assistant, with the aim to retrieve particular situations of interest, make predictions for a given tactical variant, compare and contrast, and guide through an interactive process to derive tactical suggestions. It is thus our belief that TacticAI lays the groundwork for the next-generation AI assistant for football.

We devised TacticAI as a geometric deep learning pipeline, further expanded in this section. We process labelled spatio-temporal football data into graph representations, and train and evaluate on benchmarking tasks cast as classification or regression. These steps are presented in sequence, followed by details on the employed computational architecture.

Raw corner kick data

The raw dataset consisted of 9693 corner kicks collected from the 2020–21, 2021–22, and 2022–23 (up to January 2023) Premier League seasons. The dataset was provided by Liverpool FC and comprises four separate data sources, described below.

Our primary data source is spatio-temporal trajectory frames (tracking data), which tracked all on-pitch players and the ball, for each match, at 25 frames per second. In addition to player positions, their velocities are derived from position data through filtering. For each corner kick, we only used the frame in which the kick is being taken as input information.

Secondly, we also leverage event stream data, which annotated the events or actions (e.g., passes, shots and goals) that have occurred in the corresponding tracking frames.

Thirdly, the line-up data for the corresponding games, which recorded the players’ profiles, including their heights, weights and roles, is also used.

Lastly, we have access to miscellaneous game data, which contains the game days, stadium information, and pitch length and width in meters.

Graph representation and construction

We assumed that we were provided with an input graph \({{{{{{{\mathcal{G}}}}}}}}=({{{{{{{\mathcal{V}}}}}}}},\,{{{{{{{\mathcal{E}}}}}}}})\) with a set of nodes \({{{{{{{\mathcal{V}}}}}}}}\) and edges \({{{{{{{\mathcal{E}}}}}}}}\subseteq {{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) . Within the context of football games, we took \({{{{{{{\mathcal{V}}}}}}}}\) to be the set of 22 players currently on the pitch for both teams, and we set \({{{{{{{\mathcal{E}}}}}}}}={{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) ; that is, we assumed all pairs of players have the potential to interact. Further analyses, leveraging more specific choices of \({{{{{{{\mathcal{E}}}}}}}}\) , would be an interesting avenue for future work.

Additionally, we assume that the graph is appropriately featurised. Specifically, we provide a node feature matrix, \({{{{{{{\bf{X}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) , an edge feature tensor, \({{{{{{{\bf{E}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) , and a graph feature vector, \({{{{{{{\bf{g}}}}}}}}\in {{\mathbb{R}}}^{m}\) . The appropriate entries of these objects provide us with the input features for each node, edge, and graph. For example, \({{{{{{{{\bf{x}}}}}}}}}_{u}\in {{\mathbb{R}}}^{k}\) would provide attributes of an individual player \(u\in {{{{{{{\mathcal{V}}}}}}}}\) , such as position, height and weight, and \({{{{{{{{\bf{e}}}}}}}}}_{uv}\in {{\mathbb{R}}}^{l}\) would provide the attributes of a particular pair of players \((u,\, v)\in {{{{{{{\mathcal{E}}}}}}}}\) , such as their distance, and whether they belong to the same team. The graph feature vector, g , can be used to store global attributes of interest to the corner kick, such as the game time, current score, or ball position. For a simplified visualisation of how a graph neural network would process such an input, refer to Fig.  1 A.

To construct the input graphs, we first aligned the four data sources with respect to their game IDs and timestamps and filtered out 2517 invalid corner kicks, for which the alignment failed due to missing data, e.g., missing tracking frames or event labels. This filtering yielded 7176 valid corner kicks for training and evaluation. We summarised the exact information that was used to construct the input graphs in Table  2 . In particular, other than player heights (measured in centimeters (cm)) and weights (measured in kilograms (kg)), the players were anonymous in the model. For the cases in which the player profiles were missing, we set their heights and weights to 180 cm and 75 kg, respectively, as defaults. In total, we had 385 such occurrences out of a total of 213,246( = 22 × 9693) during data preprocessing. We downscaled the heights and weights by a factor of 100. Moreover, for each corner kick, we zero-centred the positions of on-pitch players and normalised them onto a 10 m × 10 m pitch, and their velocities were re-scaled accordingly. For the cases in which the pitch dimensions were missing, we used a standard pitch dimension of 110 m × 63 m as default.

We summarised the grouping of the features in Table  1 . The actual features used in different benchmark tasks may differ, and we will describe this in more detail in the next section. To focus on modelling the high-level tactics played by the attacking and defending teams, other than a binary indicator for ball possession—which is 1 for the corner kick taker and 0 for all other players—no information of ball movement, neither positions nor velocities, was used to construct the input graphs. Additionally, we do not have access to the player’s vertical movement, therefore only information on the two-dimensional movements of each player is provided in the data. We do however acknowledge that such information, when available, would be interesting to consider in a corner kick outcome predictor, considering the prevalence of aerial battles in corners.

Benchmark tasks construction

TacticAI consists of three predictive and generative models, which also correspond to three benchmark tasks implemented in this study. Specifically, (1) Receiver prediction, (2) Threatening shot prediction, and (3) Guided generation of team positions and velocities (Table  1 ). The graphs of all the benchmark tasks used the same feature space of nodes and edges, differing only in the global features.

For all three tasks, our models first transform the node features to a latent node feature matrix, \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , from which we could answer queries: either about individual players—in which case we learned a relevant classifier or regressor over the h u vectors (the rows of H )—or about the occurrence of a global event (e.g. shot taken)—in which case we classified or regressed over the aggregated player vectors, ∑ u h u . In both cases, the classifiers were trained using stochastic gradient descent over an appropriately chosen loss function, such as categorical cross-entropy for classifiers, and mean squared error for regressors.

For different tasks, we extracted the corresponding ground-truth labels from either the event stream data or the tracking data. Specifically, (1) We modelled receiver prediction as a node classification task and labelled the first player to touch the ball after the corner was taken as the target node. This player could be either an attacking or defensive player. (2) Shot prediction was modelled as graph classification. In particular, we considered a next-ball-touch action by the attacking team as a shot if it was a direct corner, a goal, an aerial, hit on the goalposts, a shot attempt saved by the goalkeeper, or missing target. This yielded 1736 corners labelled as a shot being taken, and 5440 corners labelled as a shot not being taken. (3) For guided generation of player position and velocities, no additional label was needed, as this model relied on a self-supervised reconstruction objective.

The entire dataset was split into training and evaluation sets with an 80:20 ratio through random sampling, and the same splits were used for all tasks.

Graph neural networks

The central model of TacticAI is the graph neural network (GNN) 9 , which computes latent representations on a graph by repeatedly combining them within each node’s neighbourhood. Here we define a node’s neighbourhood, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) , as the set of all first-order neighbours of node u , that is, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}=\{v\,| \,(v,\, u)\in {{{{{{{\mathcal{E}}}}}}}}\}\) . A single GNN layer then transforms the node features by passing messages between neighbouring nodes 17 , following the notation of related work 10 , and the implementation of the CLRS-30 benchmark baselines 18 :

where \(\psi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) and \(\phi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{{k}^{{\prime} }}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) are two learnable functions (e.g. multilayer perceptrons), \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t)}\) are the features of node u after t GNN layers, and ⨁ is any permutation-invariant aggregator, such as sum, max, or average. By definition, we set \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(0)}={{{{{{{{\bf{x}}}}}}}}}_{u}\) , and iterate Eq. ( 2 ) for T steps, where T is a hyperparameter. Then, we let \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})={{{{{{{{\bf{H}}}}}}}}}^{(T)}\) be the final node embeddings coming out of the GNN.

It is well known that Eq. ( 2 ) is remarkably general; it can be used to express popular models such as Transformers 19 as a special case, and it has been argued that all discrete deep learning models can be expressed in this form 20 , 21 . This makes GNNs a perfect framework for benchmarking various approaches to modelling player–player interactions in the context of football.

Different choices of ψ , ϕ and ⨁ yield different architectures. In our case, we utilise a message function that factorises into an attentional mechanism, \(a:{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {\mathbb{R}}\) :

yielding the graph attention network (GAT) architecture 12 . In our work, specifically, we use a two-layer multilayer perceptron for the attentional mechanism, as proposed by GATv2 11 :

where \({{{{{{{{\bf{W}}}}}}}}}_{1},\, {{{{{{{{\bf{W}}}}}}}}}_{2}\in {{\mathbb{R}}}^{k\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{e}\in {{\mathbb{R}}}^{l\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{g}\in {{\mathbb{R}}}^{m\times h}\) and \({{{{{{{\bf{a}}}}}}}}\in {{\mathbb{R}}}^{h}\) are the learnable parameters of the attentional mechanism, and LeakyReLU is the leaky rectified linear activation function. This mechanism computes coefficients of interaction (a single scalar value) for each pair of connected nodes ( u ,  v ), which are then normalised across all neighbours of u using the \({{{{{{{\rm{softmax}}}}}}}}\) function.

Through early-stage experimentation, we have ascertained that GATs are capable of matching the performance of more generic choices of ψ (such as the MPNN 17 ) while being more scalable. Hence, we focus our study on the GAT model in this work. More details can be found in the subsection “Ablation study” section.

Geometric deep learning

In spite of the power of Eq. ( 2 ), using it in its full generality is often prone to overfitting, given the large number of parameters contained in ψ and ϕ . This problem is exacerbated in the football analytics domain, where gold-standard data is generally very scarce—for example, in the English Premier League, only a few hundred games are played every season.

In order to tackle this issue, we can exploit the immense regularity of data arising from football games. Strategically equivalent game states are also called transpositions, and symmetries such as arriving at the same chess position through different move sequences have been exploited computationally since the 1960s 22 . Similarly, game rotations and reflections may yield equivalent strategic situations 23 . Using the blueprint of geometric deep learning (GDL) 10 , we can design specialised GNN architectures that exploit this regularity.

That is, geometric deep learning is a generic methodology for deriving mathematical constraints on neural networks, such that they will behave predictably when inputs are transformed in certain ways. In several important cases, these constraints can be directly resolved, directly informing neural network architecture design. For a comprehensive example of point clouds under 3D rotational symmetry, see Fuchs et al. 24 .

To elucidate several aspects of the GDL framework on a high level, let us assume that there exists a group of input data transformations (symmetries), \({\mathfrak{G}}\) under which the ground-truth label remains unchanged. Specifically, if we let y ( X ,  E ,  g ) be the label given to the graph featurised with X ,  E ,  g , then for every transformation \({\mathfrak{g}}\in {\mathfrak{G}}\) , the following property holds:

This condition is also referred to as \({\mathfrak{G}}\) -invariance. Here, by \({\mathfrak{g}}({{{{{{{\bf{X}}}}}}}})\) we denote the result of transforming X by \({\mathfrak{g}}\) —a concept also known as a group action. More generally, it is a function of the form \({\mathfrak{G}}\times {{{{{{{\mathcal{S}}}}}}}}\to {{{{{{{\mathcal{S}}}}}}}}\) for some state set \({{{{{{{\mathcal{S}}}}}}}}\) . Note that a single group element, \({\mathfrak{g}}\in {\mathfrak{G}}\) can easily produce different actions on different \({{{{{{{\mathcal{S}}}}}}}}\) —in this case, \({{{{{{{\mathcal{S}}}}}}}}\) could be \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) ( X ), \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) ( E ) and \({{\mathbb{R}}}^{m}\) ( g ).

It is worth noting that GNNs may also be derived using a GDL perspective if we set the symmetry group \({\mathfrak{G}}\) to \({S}_{| {{{{{{{\mathcal{V}}}}}}}}}|\) , the permutation group of \(| {{{{{{{\mathcal{V}}}}}}}}|\) objects. Owing to the design of Eq. ( 2 ), its outputs will not be dependent on the exact permutation of nodes in the input graph.

Frame averaging

A simple mechanism to enforce \({\mathfrak{G}}\) -invariance, given any predictor \({f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , performs frame averaging across all \({\mathfrak{G}}\) -transformed inputs:

This ensures that all \({\mathfrak{G}}\) -transformed versions of a particular input (also known as that input’s orbit) will have exactly the same output, satisfying Eq. ( 5 ). A variant of this approach has also been applied in the AlphaGo architecture 25 to encode symmetries of a Go board.

In our specific implementation, we set \({\mathfrak{G}}={D}_{2}=\{{{{{{{{\rm{id}}}}}}}},\leftrightarrow,\updownarrow,\leftrightarrow \updownarrow \}\) , the dihedral group. Exploiting D 2 -invariance allows us to encode quadrant symmetries. Each element of the D 2 group encodes the presence of vertical or horizontal reflections of the input football pitch. Under these transformations, the pitch is assumed completely symmetric, and hence many predictions, such as which player receives the corner kick, or takes a shot from it, can be safely assumed unchanged. As an example of how to compute transformed features in Eq. ( 6 ), ↔( X ) horizontally reflects all positional features of players in X (e.g. the coordinates of the player), and negates the x -axis component of their velocity.

Group convolutions

While the frame averaging approach of Eq. ( 6 ) is a powerful way to restrict GNNs to respect input symmetries, it arguably misses an opportunity for the different \({\mathfrak{G}}\) -transformed views to interact while their computations are being performed. For small groups such as D 2 , a more fine-grained approach can be assumed, operating over a single GNN layer in Eq. ( 2 ), which we will write shortly as \({{{{{{{{\bf{H}}}}}}}}}^{(t)}={g}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{{\bf{H}}}}}}}}}^{(t-1)},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) . The condition that we need a symmetry-respecting GNN layer to satisfy is as follows, for all transformations \({\mathfrak{g}}\in {\mathfrak{G}}\) :

that is, it does not matter if we apply \({\mathfrak{g}}\) it to the input or the output of the function \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) —the final answer is the same. This condition is also referred to as \({\mathfrak{G}}\) -equivariance, and it has recently proved to be a potent paradigm for developing powerful GNNs over biochemical data 24 , 26 .

To satisfy D 2 -equivariance, we apply the group convolution approach 13 . Therein, views of the input are allowed to directly interact with their \({\mathfrak{G}}\) -transformed variants, in a manner very similar to grid convolutions (which is, indeed, a special case of group convolutions, setting \({\mathfrak{G}}\) to be the translation group). We use \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) to denote the \({\mathfrak{g}}\) -transformed view of the latent node features at layer t . Omitting E and g inputs for brevity, and using our previously designed layer \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) as a building block, we can perform a group convolution as follows:

Here, ∥ is the concatenation operation, joining the two node feature matrices column-wise; \({{\mathfrak{g}}}^{-1}\) is the inverse transformation to \({\mathfrak{g}}\) (which must exist as \({\mathfrak{G}}\) is a group); and \({{\mathfrak{g}}}^{-1}{\mathfrak{h}}\) is the composition of the two transformations.

Effectively, Eq. ( 8 ) implies our D 2 -equivariant GNN needs to maintain a node feature matrix \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) for every \({\mathfrak{G}}\) -transformation of the current input, and these views are recombined by invoking \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) on all pairs related together by applying a transformation \({\mathfrak{h}}\) . Note that all reflections are self-inverses, hence, in D 2 , \({\mathfrak{g}}={{\mathfrak{g}}}^{-1}\) .

It is worth noting that both the frame averaging in Eq. ( 6 ) and group convolution in Eq. ( 8 ) are similar in spirit to data augmentation. However, whereas standard data augmentation would only show one view at a time to the model, a frame averaging/group convolution architecture exhaustively generates all views and feeds them to the model all at once. Further, group convolutions allow these views to explicitly interact in a way that does not break symmetries. Here lies the key difference between the two approaches: frame averaging and group convolutions rigorously enforce the symmetries in \({\mathfrak{G}}\) , whereas data augmentation only provides implicit hints to the model about satisfying them. As a consequence of the exhaustive generation, Eqs. ( 6 ) and ( 8 ) are only feasible for small groups like D 2 . For larger groups, approaches like Steerable CNNs 27 may be employed.

Network architectures

While the three benchmark tasks we are performing have minor differences in the global features available to the model, the neural network models designed for them all have the same encoder–decoder architecture. The encoder has the same structure in all tasks, while the decoder model is tailored to produce appropriately shaped outputs for each benchmark task.

Given an input graph, TacticAI’s model first generates all relevant D 2 -transformed versions of it, by appropriately reflecting the player coordinates and velocities. We refer to the original input graph as the identity view, and the remaining three D 2 -transformed graphs as reflected views.

Once the views are prepared, we apply four group convolutional layers (Eq. ( 8 )) with a GATv2 base model (Eqs. ( 3 ) and ( 4 )) as the \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) function. Specifically, this means that, in Eqs. ( 3 ) and ( 4 ), every instance of \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t-1)}\) is replaced by the concatenation of \({({{{{{{{{\bf{h}}}}}}}}}_{{\mathfrak{h}}}^{(t-1)})}_{u}\parallel {({{{{{{{{\bf{h}}}}}}}}}_{{{\mathfrak{g}}}^{-1}{\mathfrak{h}}}^{(t-1)})}_{u}\) . Each GATv2 layer has eight attention heads and computes four latent features overall per player. Accordingly, once the four group convolutions are performed, we have a representation of \({{{{{{{\bf{H}}}}}}}}\in {{\mathbb{R}}}^{4\times 22\times 4}\) , where the first dimension corresponds to the four views ( \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}},\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\updownarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow \updownarrow }\in {{\mathbb{R}}}^{22\times 4}\) ), the second dimension corresponds to the players (eleven on each team), and the third corresponds to the 4-dimensional latent vector for each player node in this particular view. How this representation is used by the decoder depends on the specific downstream task, as we detail below.

For receiver prediction, which is a fully invariant function (i.e. reflections do not change the receiver), we perform simple frame averaging across all views, arriving at

and then learn a node-wise classifier over the rows of \({{{{{{{{\bf{H}}}}}}}}}^{{{{{{{{\rm{node}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . We further decode H node into a logit vector \({{{{{{{\bf{O}}}}}}}}\in {{\mathbb{R}}}^{22}\) with a linear layer before computing the corresponding softmax cross entropy loss.

For shot prediction, which is once again fully invariant (i.e. reflections do not change the probability of a shot), we can further average the frame-averaged features across all players to get a global graph representation:

and then learn a binary classifier over \({{{{{{{{\bf{h}}}}}}}}}^{{{{{{{{\rm{graph}}}}}}}}}\in {{\mathbb{R}}}^{4}\) . Specifically, we decode the hidden vector into a single logit with a linear layer and compute the sigmoid binary cross-entropy loss with the corresponding label.

For guided generation (position/velocity adjustments), we generate the player positions and velocities with respect to a particular outcome of interest for the human coaches, predicted over the rows of the hidden feature matrix. For example, the model may adjust the defensive setup to decrease the shot probability by the attacking team. The model output is now equivariant rather than invariant—reflecting the pitch appropriately reflects the predicted positions and velocity vectors. As such, we cannot perform frame averaging, and take only the identity view’s features, \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . From this latent feature matrix, we can then learn a conditional distribution from each row, which models the positions or velocities of the corresponding player. To do this, we extend the backbone encoder with conditional variational autoencoder (CVAE 28 , 29 ). Specifically, for the u -th row of H id , h u , we first map its latent embedding to the parameters of a two-dimensional Gaussian distribution \({{{{{{{\mathcal{N}}}}}}}}({\mu }_{u}| {\sigma }_{u})\) , and then sample the coordinates and velocities from this distribution. At training time, we can efficiently propagate gradients through this sampling operation using the reparameterisation trick 28 : sample a random value \({\epsilon }_{u} \sim {{{{{{{\mathcal{N}}}}}}}}(0,1)\) for each player from the unit Gaussian distribution, and then treat μ u  +  σ u ϵ u as the sample for this player. In what follows, we omit edge features for brevity. For each corner kick sample X with the corresponding outcome o (e.g. a binary value indicating a shot event), we extend the standard VAE loss 28 , 29 to our case of outcome-conditional guided generation as

where h u is the player embedding corresponding to the u th row of H id , and \({\mathbb{KL}}\) is Kullback–Leibler (KL) divergence. Specifically, the first term is the generation loss between the real player input x u and the reconstructed sample decoded from h u with the decoder p ϕ . Using the KL term, the distribution of the latent embedding h u is regularised towards p ( h u ∣ o ), which is a multivariate Gaussian in our case.

A complete high-level summary of the generic encoder–decoder equivariant architecture employed by TacticAI can be summarised in Supplementary Fig.  2 . In the following section, we will provide empirical evidence for justifying these architectural decisions. This will be done through targeted ablation studies on our predictive benchmarks (receiver prediction and shot prediction).

Ablation study

We leveraged the receiver prediction task as a way to evaluate various base model architectures, and directly quantitatively assess the contributions of geometric deep learning in this context. We already see that the raw corner kick data can be better represented through geometric deep learning, yielding separable clusters in the latent space that could correspond to different attacking or defending tactics (Fig.  2 ). In addition, we hypothesise that these representations can also yield better performance on the task of receiver prediction. Accordingly, we ablate several design choices using deep learning on this task, as illustrated by the following four questions:

Does a factorised graph representation help? To assess this, we compare it against a convolutional neural network (CNN 30 ) baseline, which does not leverage a graph representation.

Does a graph structure help? To assess this, we compare against a Deep Sets 31 baseline, which only models each node in isolation without considering adjacency information—equivalently, setting each neighbourhood \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) to a singleton set { u }.

Are attentional GNNs a good strategy? To assess this, we compare against a message passing neural network 32 , MPNN baseline, which uses the fully potent GNN layer from Eq. ( 2 ) instead of the GATv2.

Does accounting for symmetries help? To assess this, we compare our geometric GATv2 baseline against one which does not utilise D 2 group convolutions but utilises D 2 frame averaging, and one which does not explicitly utilise any aspect of D 2 symmetries at all.

Each of these models has been trained for a fixed budget of 50,000 training steps. The test top- k receiver prediction accuracies of the trained models are provided in Supplementary Table  2 . As already discussed in the section “Results”, there is a clear advantage to using a full graph structure, as well as directly accounting for reflection symmetry. Further, the usage of the MPNN layer leads to slight overfitting compared to the GATv2, illustrating how attentional GNNs strike a good balance of expressivity and data efficiency for this task. Our analysis highlights the quantitative benefits of both graph representation learning and geometric deep learning for football analytics from tracking data. We also provide a brief ablation study for the shot prediction task in Supplementary Table  3 .

Training details

We train each of TacticAI’s models in isolation, using NVIDIA Tesla P100 GPUs. To minimise overfitting, each model’s learning objective is regularised with an L 2 norm penalty with respect to the network parameters. During training, we use the Adam stochastic gradient descent optimiser 33 over the regularised loss.

All models, including baselines, have been given an equal hyperparameter tuning budget, spanning the number of message passing steps ({1, 2, 4}), initial learning rate ({0.0001, 0.00005}), batch size ({128, 256}) and L 2 regularisation coefficient ({0.01, 0.005, 0.001, 0.0001, 0}). We summarise the chosen hyperparameters of each TacticAI model in Supplementary Table  1 .

Data availability

The data collected in the human experiments in this study have been deposited in the Zenodo database under accession code https://zenodo.org/records/10557063 , and the processed data which is used in the statistical analysis and to generate the relevant figures in the main text are available under the same accession code. The input and output data generated and/or analysed during the current study are protected and are not available due to data privacy laws and licensing restrictions. However, contact details of the input data providers are available from the corresponding authors on reasonable request.

Code availability

All the core models described in this research were built with the Graph Neural Network processors provided by the CLRS Algorithmic Reasoning Benchmark 18 , and their source code is available at https://github.com/google-deepmind/clrs . We are unable to release our code for this work as it was developed in a proprietary context; however, the corresponding authors are open to answer specific questions concerning re-implementations on request. For general data analysis, we used the following freely available packages: numpy v1.25.2 , pandas v1.5.3 , matplotlib v3.6.1 , seaborn v0.12.2 and scipy v1.9.3 . Specifically, the code of the statistical analysis conducted in this study is available at https://zenodo.org/records/10557063 .

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Acknowledgements

We gratefully acknowledge the support of James French, Timothy Waskett, Hans Leitert and Benjamin Hervey for their extensive efforts in analysing TacticAI’s outputs. Further, we are thankful to Kevin McKee, Sherjil Ozair and Beatrice Bevilacqua for useful technical discussions, and Marc Lanctôt and Satinder Singh for reviewing the paper prior to submission.

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These authors contributed equally: Zhe Wang, Petar Veličković, Daniel Hennes.

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Google DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK

Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess & Demis Hassabis

Liverpool FC, AXA Training Centre, Simonswood Lane, Kirkby, Liverpool, L33 5XB, UK

William Spearman

Liverpool FC, Kirkby, UK

University of Alberta, Amii, Edmonton, AB, T6G 2E8, Canada

Michael Bowling

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Contributions

Z.W., D. Hennes, L.P. and K.T. coordinated and organised the research effort leading to this paper. P.V. and Z.W. developed the core TacticAI models. Z.W., W.S. and I.G. prepared the Premier League corner kick dataset used for training and evaluating these models. P.V., Z.W., D. Hennes and N.T. designed the case study with human experts and Z.W. and P.V. performed the qualitative evaluation and statistical analysis of its outcomes. Z.W., P.V., D. Hennes, N.T., L.P., M. Kaisers, Y.B., R.E., L.K.W., F.P., W.S., I.G., N.H., M.B., D. Hassabis and K.T. contributed to writing the paper and providing feedback on the final manuscript. J.C., Y.Y., A.R., M. Khan, N.B., P.S. and P.M. contributed valuable technical and implementation discussions throughout the work’s development.

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Correspondence to Zhe Wang , Petar Veličković or Karl Tuyls .

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The authors declare no competing interests but the following competing interests: TacticAI was developed during the course of the Authors’ employment at Google DeepMind and Liverpool Football Club, as applicable to each Author.

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Wang, Z., Veličković, P., Hennes, D. et al. TacticAI: an AI assistant for football tactics. Nat Commun 15 , 1906 (2024). https://doi.org/10.1038/s41467-024-45965-x

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Title: mm1: methods, analysis & insights from multimodal llm pre-training.

Abstract: In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

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Nanoparticles in Drug Delivery: From History to Therapeutic Applications

Obaid afzal.

1 Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia

Abdulmalik S. A. Altamimi

Muhammad shahid nadeem.

2 Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Sami I. Alzarea

3 Department of Pharmacology, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia

Waleed Hassan Almalki

4 Department of Pharmacology, College of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia

5 Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore, Lahore 54000, Pakistan

Bismillah Mubeen

Bibi nazia murtaza.

6 Department of Zoology, Abbottabad University of Science and Technology (AUST), Abbottabad 22310, Pakistan

Saima Iftikhar

7 School of Biological Sciences, University of Punjab, Lahore 54000, Pakistan

8 Department of Pharmacy, COMSATS University, Abbottabad 22020, Pakistan

Imran Kazmi

Associated data.

Not applicable.

Current research into the role of engineered nanoparticles in drug delivery systems (DDSs) for medical purposes has developed numerous fascinating nanocarriers. This paper reviews the various conventionally used and current used carriage system to deliver drugs. Due to numerous drawbacks of conventional DDSs, nanocarriers have gained immense interest. Nanocarriers like polymeric nanoparticles, mesoporous nanoparticles, nanomaterials, carbon nanotubes, dendrimers, liposomes, metallic nanoparticles, nanomedicine, and engineered nanomaterials are used as carriage systems for targeted delivery at specific sites of affected areas in the body. Nanomedicine has rapidly grown to treat certain diseases like brain cancer, lung cancer, breast cancer, cardiovascular diseases, and many others. These nanomedicines can improve drug bioavailability and drug absorption time, reduce release time, eliminate drug aggregation, and enhance drug solubility in the blood. Nanomedicine has introduced a new era for drug carriage by refining the therapeutic directories of the energetic pharmaceutical elements engineered within nanoparticles. In this context, the vital information on engineered nanoparticles was reviewed and conferred towards the role in drug carriage systems to treat many ailments. All these nanocarriers were tested in vitro and in vivo. In the coming years, nanomedicines can improve human health more effectively by adding more advanced techniques into the drug delivery system.

1. Introduction

Drug delivery systems (DDSs) have been used in past eras to treat numerous ailments. All medicines rely on pharmacologic active metabolites (drugs) to treat diseases [ 1 ]. Some of the drugs are designed as the inactive precursor, but they become active when transformed in the body [ 2 ]. Their effectiveness depends on the route of administration. In conventional drug delivery systems (CDDSs), drugs were delivered usually via oral, nasal, inhaled, mucosal, and shot methods [ 3 ]. The conventionally delivered drugs were absorbed less, distributed randomly, damaged unaffected areas, were excreted early, and took a prolonged time to cure the disease [ 4 ]. They were less effective due to many hurdles like their enzymatic degradation or disparity in pH, many mucosal barriers, and off-the-mark effects, and their immediate release enhanced toxicity in blood [ 5 ].

Due to all such reasons, the controlled-release drug delivery system was developed. Such evolution in the DDS enhances drug effectiveness in many ways [ 6 ]. DDSs have been engineered in recent years to control drug release [ 7 ]. Such engineered DDSs used various novel strategies for controlled drug release into the diseased areas. These strategies were erodible material, degradable material, matrix, hydrogel, osmotic pump, and reservoir [ 8 ]. They all provided a medium for the medicines to deliver at the desired sites like tissues, cells, or organs. In these approaches, drugs are often available for many diseases [ 9 ]. Such strategies were unsuccessful due to lower distribution, less solubility, higher drug aggregation, less target selection, and poor effects for disease treatment [ 10 ]. Moreover, drug development is the most expensive, intricate, and time-consuming process [ 5 ]. The innovative drug findings involved the identification of new chemical entities (NCEs), [ 11 ] having the vital distinguishing characteristics of drug capacity and pharmaceutical chemistry. This methodology, however, was confirmed to be less effective in terms of the overall attainment percentage [ 12 ], as 40% of drug development was botched due to its changeable responses and unpredicted noxiousness in humans [ 13 ]. From past decades until now, drug development and its delivery are shifting from the micro to the nano level to prolong life expectancy by revolutionizing drug delivery systems ( Figure 1 ) [ 14 ].

Illustration of how traditional medications were administered without the use of nanocarriers and harm was done to healthy organs or cells. In contrast, modern procedures use nanomedicines to transport medications to specific parts of the body.

In 1959, Feynman was the first physicist to introduce the notion of nanotechnology in the lecture entitled “There’s Plenty of oom at the Bottom”. This concept initiated remarkable developments in the arena of nanotechnology [ 15 ]. Nanotechnology is the study of extremely tiny things and is basically the hub of all science disciplines including physics, chemistry, biology, engineering, information technology, electronics, and material science [ 16 ]. The structures measured with nanotechnology range from 1–100 nm at the nanoscale level [ 17 ]. Nanoparticles have different material characteristics because of submicroscopic size and also provide practical implementations in a wide range of fields including engineering, drug delivery, nanomedicine, environmental indemnification, and catalysis, as well as target diseases such as melanoma and cardiovascular diseases (CVD), skin diseases, liver diseases, and many others [ 18 ].

Therefore, medicines linked with nanotechnology can enhance efficiency of medicines and their bioavailability [ 19 ]. The relation of nanoparticles to biomedicine was demonstrated in late the 1970s, and over 10,000 publications have referred to this association with the term “nanomedicine”. Almost thirty papers on this term were accessible by 2005 [ 20 ].

After 10 to 12 years, Web of Science published more than 1000 nanomedicine articles in 2015 and most of the articles relating nanoparticles (NPs) for biomedical usage [ 21 ]. Nanocarriers such as dendrimers, liposomes, peptide-based nanoparticles, carbon nano tubes, quantum dots, polymer-based nanoparticles, inorganic vectors, lipid-based nanoparticles, hybrid NPs, and metal nanoparticles are the advanced forms of NPs [ 22 ]. Nanoparticles are nowadays a growing arena for drug delivery, microfluidics, biosensors, microarrays, and tissue micro-engineering for the specialized treatment of diseases [ 23 , 24 , 25 ].

Nanoparticles are less effective and can treat cancer by selectively killing all cancerous cells [ 26 ]. In 2015, the Food and Drug Administration (FDA) approved the clinical trials of onivyde nanomedicine in the treatment of cancer [ 27 ]. The characteristic properties of nanocarriers are physicochemical properties, supporting the drugs by improving solubility, degradation, clearance, targeting, theranostics, and combination therapy [ 28 ]. Studies on nanomedicine based on protein used for drug delivery in which various protein subunits combine to deliver medicine on site to a specific tumor have been reported [ 29 ]. Many altered kinds and forms of nanocarriers arranged to carry medicine are protein-based podiums, counting several protein coops, nanoparticles, hydrogels, films, microspheres, tiny rods, and minipellets [ 30 ]. All proteins, including ferritin–protein coop, the small heat shock protein (sHsp) cage, plant-derived viral capsids, albumin, soy and whey protein, collagen, and gelatin-implemented proteins are characterized for drug carriage [ 31 ].

The nanomedicines are escorted in a new-fangled epoch, meant for drug carriage by refining the therapeutic directories of the energetic pharmacological elements engineered inside nanoparticles [ 32 ]. In this epoch, nanomedicine-based targeted-design structures can deliver multipurpose freight with favorable pharmacokinetics and capitalized so as to enhance drug specificity, usefulness, and safety, as shown in ( Figure 2 ) [ 33 ]. The failure of chemotherapeutic approaches has increased the recurrence chances of disease, which enhances the complexity of lethal diseases [ 34 ].

Aids of using nanomedicine platform for delivering drugs to the tumor complex.

Petros and his colleague reported a study about mid-19th century work on nanotechnology. As they reported, polymers and drugs were conjugated in 1955 [ 35 ], the first controlled-release polymer device appeared in 1964, the liposome was discovered by Bangham in 1965, albumin-based NPs were reported in 1972, liposome-based drugs were formulated in 1973, the first micelle was formulated and approved in 1983, the FDA approved the first controlled formulation in 1989, and first polyethylene glycol (PEG) conjugated with protein entered the market in 1990 [ 36 ]. Further studies have produced incredibly encouraging results for treating a variety of disorders ( Table 1 ).

Evolution of nanoparticles from 1991 to 2022 in detail discussed here.

3. Recent Approaches Used in Drug Carriage System for Treatment of Various Diseases

3.1. brain drug delivery system and its types.

Under the most pathological circumstances of diseases such as strokes, seizures, multiple sclerosis, AIDS, diabetes, glioma, Alzheimer’s disease, and Parkinson’s disease, the blood–brain barrier (BBB) is disrupted [ 103 ]. An important reason for the breakdown of the blood–brain barrier is the remodeling of the protein complex in intra-endothelial junctions under the pathological conditions [ 104 ]. Normally, the blood–brain barrier acts to maintain blood–brain homeostasis by preventing entry of macromolecules and micromolecules from the blood [ 105 ]. If a drug crosses the BBB, it restricts accumulation of the drug in the intracerebral region of brain, and bioavailability is reduced, due to which brain diseases cannot be treated [ 106 ]. Therefore, the optimal drug delivery system (DDS) is a cell membrane DDS, virus-based DDS, or exosome-based DDS designed for BBB penetrability, lesion-targeting ability, and standard safety [ 107 ]. For the cure of brain diseases, the nanocarrier-assisted intranasal drug carriage system is widely used [ 108 ]. Now, at the advanced level, drugs poorly distributed to the brain can be loaded into a nanocarrier-based system, which would interact well with the endothelial micro vessel cells at the BBB and nasal mucosa to increase drug absorption time and the olfactory nerve fibers to stimulate straight nose-to-brain delivery [ 109 ], thus greater drug absorption in brain parenchyma through the secondary nose-to-blood-to-brain pathway [ 110 ]. The current strategies used are viral vectors, nanoparticles, exosomes, brain permeability enhancers, delivery through active transporters in the BBB, alteration of administration route, nanoparticles for the brain, and imaging/diagnostics under diseased conditions [ 111 ].

3.1.1. Role of Nanocarriers in Alzheimer’s Disease

Alzheimer’s disease is one of the fastest growing neurodegenerative diseases in the elderly population. Clinically, it is categorized by abstraction, damage to verbal access, and diminishing in spatial skills and reasoning [ 112 ]. Furthermore, engrossment of amyloid β (Aβ) aggregation and anxiety in the brain have significant parts [ 113 ]. The treatment of different diseases with nanotechnology-based drug delivery uses nanotechnology-based approaches [ 114 ]. In Alzheimer’s diseases, polymeric nanoparticles, liposomes, solid lipid nanoparticles, nano-emulsions, micro-emulsions, and liquid-crystals are used for treatment.

Polymeric Nanoparticles

• I. The drug Tacrine was loaded on polymeric nanoparticles and administered through an intravenous route. It enhanced the concentration of tacrine inside the brain and also reduced the whole-dose quantity [ 115 ].
• II. Rivastigmine drug was loaded on polymeric nanoparticles and administered through an intravenous route. It enhanced learning and memory capacities [ 116 ].

Solid Lipid Nanoparticles (SLNPs)

SLNPs enhanced drug retention in the brain area, raising absorption across the BBB [ 117 ]. Some of the drug’s effects are listed below.

• I. Piperine drug is loaded on solid lipid nanoparticles through an intraperitoneal route inside the brain to decrease plaques and masses and to increase AChE enzyme activity [ 118 ].
• II. Huperzine A improved cognitive functions. No main irritation was detected in rat skin when the drug was loaded on SLNPs in an in vitro study [ 119 ].

In recent reports, the coating of SLNPs with polysorbate enhances drug bioavailability [ 120 , 121 ]. Some of the coated NPs are listed below.

• I. The drug clozapine was loaded on a Dynasan 116 [Tripalmitin] lipid matrix coated with surfactant Poloxamer 188, Epikuron 200 to unload the drug safely into the brain microenvironment [ 122 , 123 ].
• II. Vitamin A was loaded on a lipid matrix Glyceryl behenate with coated surfactant hydroxypropyl distarch to unload the drug safely across the BBB [ 124 , 125 ].
• III. Diminazine was loaded on a stearic acid matrix coated with polysorbate 80 to deliver to an infected area safely [ 126 , 127 ].
• IV. Doxorubicin was loaded on stearic acid SLNs coated with Taurodeoxycholate surfactant to deliver the drug without reducing its effectiveness [ 128 , 129 ].

Liposomes have gained attention as auspicious tactics for brain-targeted drug delivery [ 130 ]. The recorded beneficial features of liposomes are their capacity to integrate and carry a large quantity of drugs and their likelihood to adorn their exterior with diverse ligands [ 131 , 132 ].

• Curcumin–PEG derivative was loaded on liposomes and showed high affinity on senile plaques in an ex vivo experiment. Furthermore, in vitro it demonstrated the ability for Aβ aggregation and was taken inside by the BBB in a rat model [ 133 ].
• Folic acid was loaded on liposomes, administered through an intranasal route and absorbed through the nasal cavity [ 134 ].

Nanoemulsions

• I. Beta-Asarone was loaded on nanoemulsions, administered through an intranasal route, and enhanced bioavailability [ 130 ].

Micro Emulsion

• I. Tacrine was loaded on a microemulsion and improved memory. Such nanoparticles absorbed rapidly via the nose to the brain through an intranasal route [ 135 ].

Liquid Crystals

• I. T. divaricate was loaded on liquid crystals and injected through a transdermal route. It increased permanency of the drug in designs and also increased skin infusion and retention [ 136 ].

3.1.2. Role of Nanocarriers in Parkinson’s Disease (PD)

Parkinson’s disease is considered the second most common neurological ailment, and it faces problems in reliable drug delivery for treatment and diagnosis [ 137 ]. The conventional anti-Parkinson’s drug is Levodopa , but it experiences low bioavailability and deprived transfer to the brain; this is the most thought-provoking problem [ 138 ]. To solve this problem, nanotechnology comes to the fore with insightful solutions to solve this problem. Various nanoparticles like metal nanoparticles, quantum dots, cerium oxide nanoparticles, organic nanoparticles, liposomes, and gene therapy are used in PD treatment [ 139 ]. All these nanoparticles enable drugs to enter through numerous ways across the blood–brain barrier (BBB) [ 140 ]. In the current study, Bhattamisra et al. reported Rotigotine drug loaded on chitosan NPs in human SH-SY5Y neuroblastoma cells and delivered from the nose to the brain in rat model of Parkinson’s disease. A study of the pharmacokinetic data proposed that the intranasal route is the best path for a straight channel of rotigotine to the brain [ 125 ].

Ropinirole (RP)

Ropinirole (RP) is a dopamine agonist used for Parkinson’s treatment. RP-loaded solid lipid nanoparticles (RP-SLNs) with nanostructured lipid carriers (RP-NLCs) comprising hydrogel (RP-SLN-C and RP-NLC-C) formulations are better for oral and topical distribution [ 141 ]. Generally, the results confirmed that lipid nanoparticles and consistent hydrogel formulations can be measured as another carriage methodology for the upgraded oral and topical delivery of RP for the active treatment of PD [ 142 ]. Neurodegenerative pathologies such as AD and PD can be treated with solid lipid nanoparticles, as this permits the drug to cross the BBB and reach the damaged area of the central nervous system [ 143 ].

3.2. Mechanism of Nanoparticles’ Brain Drug Delivery (across BBB)

The NPs are commonly administered via intranasal, intraventricular, intraparenchymal routes. All these routes enabled nanoparticles to cross the BBB due to their small size. When nanoparticles reach the BBB, several mechanisms are used, like receptor-mediated mechanisms, active transport, and passive transport to deliver nanoparticles into the brain. Nanoparticles are small in size, can diffuse passively across the endothelial cells of the BBB, and can interact favorably with brain receptors and recognize ligands for interaction ( Figure 3 ) [ 144 ].

Diagram showing the mechanism of targeted drug delivery across BBB in brain microenvironment. Piperine loaded on SLNPs is injected intraperitonially, across BBB efferently to stop plaque formation. Polymeric nanoparticles are used for Tacrine delivery inside the brain, folic acid are loaded on the liposomes crossing blood–brain barrier to treat Alzheimer’s disease, while nanoemulsions and SLNP are loaded with drugs used to deliver medicines inside the targeted brain area to cure Parkinson’s disease.

3.3. Advantages and Disadvantages of Nanomedicines

When employed for brain illnesses, nanomedicines have both benefits and drawbacks ( Table 2 ).

Advantages and disadvantages of nanomedicine.

4. Nanocarriers Role in Major Cancers

4.1. brain cancer.

Brain malignancy is the most critical disease in the sense of treatment [ 150 ]. Malignancies of the brain are most difficult to treat due to limits imposed by the blood–brain barrier [ 151 ]. The brain microvascular endothelium is present in the BBB and creates barriers that distinguish blood from the neural tissues of the brain [ 152 ]. The BBB prevents the entry of harmful toxins, xenobiotic and other metabolites from entering the brain [ 153 ]. The majority of brain cancers include glioma and glioblastoma. Both of these are among the most lethal forms of brain cancer [ 154 ]. The annual occurrence is 5.26 per 100,000 people or 17,000 new diagnoses each year. The most common treatment is radiation surgery and chemotherapy, usually implemented with with temozolomide (TMZ) [ 155 ]. Nanoparticles have a high potential to treat brain cancer because of their small size in nm, tissue-specific targeting properties, and ease in crossing the BBB [ 156 ] ( Table 3 ).

Various nanoparticles involved in brain cancer treatment in recent era.

4.2. Breast Cancer

Cancer causes major deaths all over the world. Tumors spread due to the proliferation of cells [ 171 ], which invade through the lymphatic system to various parts of the body if they becomes malignant [ 172 ]. According to WHO, the ratio of deaths globally due to cancer is assessed to be 13%, attributing 8.2 million deaths every year [ 173 ]. Breast cancer is the most recorded type of melanoma present in only females, and its severity leads to mortality more often than lung cancer [ 174 ]. In 2012, estimated female breast cancer cases were 1.7 million, with 25% of deaths all over the world [ 175 ]. In a recent study, a report published in the name of Global Cancer Statistics 2020: GLOBOCAN estimates the incidence and mortality worldwide for 36 cancers in 185 countries and provides an update on cancer internationally [ 176 ]. A reported estimate is 19.3 million new cancer cases (18.1 million excluding non-melanoma skin cancer) and almost 10 million cancer deaths (9.9 million without non-melanoma skin cancer) occurring in 2020 worldwide. Female breast cancer has exceeded lung cancer as the most frequently diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), prostate (7.3%), colorectal (10%), and stomach (5.6%) cancers [ 177 ]. For the effective treatment of breast cancer, surgery, chemotherapy, radiation therapy, hormonal therapy, and targeted therapy are performed [ 178 ]. However, nowadays, nanotechnology has gained interest for breast cancer treatment. Various organic and inorganic nanocarriers are used to deliver drugs to the specific target site [ 179 ]. Nanocarriers enhance the hydrophobicity of the anticancer drugs and promote specific target drug delivery [ 180 ]. Organic nanocarriers include polymeric nanocarriers, liposome nanocarriers, and solid lipid nanocarriers, while inorganic nanocarriers include magnetic nanocarriers, quantum dots, and carbon nanotubes (CNTs); both categories show great results towards treatment of heart diseases ( Table 4 ) [ 181 ]. The mechanism of drug delivery in breast cancer is shown in Figure 4 .

Schematic representation of mechanism of drug letrozol loaded on solid lipid nanoparticles (SLNs) and folic acid coupled to SLNs. The whole carrier was delivered inside the animal rat model to treat effects on breast cancer cell lines. Inside cytoplasm, biodegradation occurred, as well as drug release and caspases’ activation inside nucleus, causing apoptosis.

Nanoparticles’ role in treatment of breast cancer.

4.3. Lung Cancer

Lungs are basically responsible for inhalation [ 194 ]. The lung is composed airways (conveying the air inside and outside of the lungs) and alveoli (gas exchange zones) [ 195 ]. In fact, airways are comparatively tough barriers for particles to enter through, while the barrier along the alveolar wall and the capillaries is relatively fragile in the gas exchange component [ 196 ]. The huge exterior area of the alveoli and deep air blood exchange cause the alveoli to be less healthy when affected by environmental injuries. Such injuries may be the reason for some pulmonary illnesses, including lung malignancy [ 197 ]. Several nanoparticles are now being established for respiratory applications that aim at eliminating the restrictions of orthodox drugs [ 198 ] ( Table 5 ). Nanoparticles aid the cure of many lung diseases, such as asthma, tuberculosis, emphysema, cystic fibrosis, and cancer [ 199 ].

Recent discovered nanoparticle’s role in lung cancer treatment.

5. Drug Delivery Approach in Heart Diseases

Cardiovascular diseases include myocardial infraction (MI) [ 213 ], ischemic impairment, coronary artery disease (CAD), heart arrhythmias, pericardial disease, cardiomyopathy (heart muscle disease), and congenital heart disease [ 214 , 215 ]. All these illnesses are the basic main cause of mortality and morbidity in the world [ 216 ]. Cardiac diseases in humans involve incongruity in the morphogenesis of heart arrangement, functionality, and the healing and periodic shrinkage of cardiac muscles [ 217 , 218 ]. Around 50% of patients suffering from MI die within five years [ 216 ]. The insistence for a novel and effective remedy has brought about progress in direct drug carriage to the heart [ 219 ]. Modern therapeutic approaches have been developed to stop the incidence of heart failure after myocardial infarction [ 220 ]. Liposomes, silica NPs, dendrimers, cerium oxide NPs, micelles, TiO 2 NPs, stents with nano-coatings, microbubbles, and polymer–drug conjugates are used for drug delivery. Magnetic nanoparticles like magnetoliposomes (MLs) are made up of the union of liposomes and magnetic nanoparticles. They are used as magnetic-targeted drug delivery [ 221 ]. The PEGylation of MLs increases their rate of flow in the blood, and pairing of the MLs with antibodies raises the rate of active target to pretentious positions [ 222 ]. Namdari and his co-workers performed experiments in a mice model afflicted with myocardial infraction (MI). Liposomes are used with various modifications and in different ways; they are adapted to load drugs on NPs for efficient delivery inside the cell. Cationic liposomes, perfluorocarbon nanoparticles, polyelectrolyte nanoparticles, and polymeric nanoparticles are the modified forms of nanocarriers [ 223 ] ( Table 6 ).

Different forms of NPs; their experiment studies show its role in treatment of heart diseases.

6. Drug Delivery Approach in Skin Diseases

Skin diseases are follicular and cutaneous. These dermatological diseases are treated nowadays with nanotechnology. Nanoparticle delivery for cutaneous disease treatment is preferred, with minor side effects. The conventionally used creams, gels, and ointments are insufficient for delivering drugs due to low penetration in skin tissues. To address this, polymeric, lipid, and surfactant nanocarriers are used. The polymeric micelles enhance drug penetration into the skin tissue to treat skin cancer. As in this reported study, chitosan polymeric NPs, liposomes, and gold nanoparticles can treat atopic dermatitis by improving drug penetration into the dermal and epidermal layers [ 246 ]. Gold nanoparticles are extremely small in size and can penetrate easily and effectively with very low toxicity and no skin damage. As such, they are used widely in nanocarrier formulations for skin diseases.

7. Drug Delivery Approach in Bone Diseases

Bone diseases includes bone defects due to many pathological factors, such as fracture, trauma, osteoporosis, arthritis, infections, and many other diseases. In fact, bone regeneration as a disease treatment is a very complex process, due to which nanomaterials and biological materials are fused to repair bones effectively. The combination of biomaterial and nanomaterial has reduced bone implantation through the development of bone bioscaffolds [ 247 ].

Mechanism of Drug Delivery

The drugs encapsulated inside the nanoparticle is delivered through blood to the targeted area in the bones. The management of the sending nanoparticles as shown herenin ( Figure 5 ).

Mechanism of nanomedicine delivery in bone diseases.

8. Drug Delivery Approach in Blood Diseases

There are various types of blood diseases, like hemopoietic blood disorder, as well as iron deficiency, leukemia, anemia, hemophilia, platelet diseases, and blood cancer. The conventionally used chemotherapeutic system causes damage to the immune system, with high risk of mortality. Bone marrow transplant is also an expensive and intricate process. For example, thalassemia is treated with deferoxamine, a chelating agent to treat excessive iron in the blood. The siRNA-coated nanocomposite has the inhibitory activity for tumor cells in vivo [ 248 ]. The treatment of blood disorders with nanomedicine is still under investigation.

9. Future Challenges of Nanomedicines

In the field of nanomedicine, there are many innovations which show its importance in clinical and other medical aspects. Many scientists have investigated in their research how nanomedicine is involved in treating malignancies and reducing mortality and morbidity rates. However, there are also future challenges that nanomedicines have been facing until now [ 249 ]. The implementation of nanomedicine in clinical practice will face many issues with insurance companies, regulatory agencies, and the public health sector. Until now, the FDA has not developed any specific regulation for the products containing nanomaterials. Due to a lack of nanomaterial standardization and other safety issues, US agencies, such as the EPA and NIOSH, are giving less funding to these research endeavors.

10. Conclusions

Nanotechnology-based nanomedicine is a diverse field for disease treatment. Nowadays, in every sort of disease, nanotechnology is emerging as the best therapeutic to cure disease. At California University, researchers are developing methods to deliver cardiac stem cells to the heart. They attached nanovesicles that directly target injured tissue to increase the amount of stem cells there. Thus, the involvement of stem cells with nanotechnology will develop many solutions for the disease-based queries in the medical arena. However, nanomedicine and nano drugs deal with many doubts. Irregularities and toxicity and safety valuations will be the topic of development in the future. Nanotechnology will be in high demand. Nowadays, drug-targeted delivery through nanoparticles is catching the attention of pharmaceutical researchers all over the world. Nanomedicine will overcome all the side effects of traditional medicines. This nanoscale technology will be incorporated in the medical system to diagnose, transport therapeutic drugs, and detect cancer growth, according to the National Cancer Institute. Experts are trying to treat SARS-CoV-2 with nanomedicine, as nanoparticles with 10–200 nm size can detect, for site-specific transfer, SARS-CoV-2, exterminate it, and improve the immune system of the body. Nanotechnology could help to combat COVID-19 by stopping viral contamination. Highly accurate nano-based sensors will be made in the future that will quickly recognize the virus and act by spraying to protect frontline doctors and the public. Furthermore, many antiviral disinfectants are being developed through nanobiotechnology to stop virus dissemination. In the future, nanotechnology will evolve to develop drugs with high activity, less toxicity, and sustained release to target tissue. Therefore, personalized medicine and nanomedicine both will be potential therapies to treat COVID-19 successfully, as well as to treat upcoming diseases in future.

Acknowledgments

The authors are thankful to Umm Al-Qura University, Makkah, Saudi Arabia, for supporting this project (Project number 224UQU4310387DSR40).

Funding Statement

The Project was funded by Deanship of Scientific Research at Umm Al-Qura University, and this work was supported by Grant Code (Project Code: 22 UQU4310387DSR40).

Author Contributions

Conceptualization, M.S.N. and I.K.; original draft, O.A., M.S.N., B.M. and O.A.; writing—review and editing, O.A., S.I.A., A.S.A.A., A.T., B.M., B.N.M., S.I. and N.R.; funding acquisition, W.H.A. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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