Graphene has been prepared in a variety of ways. The electrical characteristics of graphene may be harmed by structural faults, imperfections, and wrinkles inside the material (Goenka et al., 2014). Different researchers reported many methods to produce several graphene-based nanocomposites.

Jaleh et al. (2017) utilized the co-precipitation method at low temperatures for the formation of graphene oxide/Fe₃O₄ (GO/Fe₃O₄) nanocomposites. Use of energy dispersive X-ray analysis, XRD and FT-IR, and TEM and SEM were used to determine the particle size and morphology of the prepared nanocomposites. Particle size was found to be smaller than 20 nm. GO/Fe₃O₄ nanocomposites were prepared using different amounts of FeCl₂.4H₂O and FeCl₃.6H₂O.

Qian et al. (2020) prepared lignin-poly (N-methylanine)-reduced graphene oxide hydrogel using a two-step method. Characteristically, a modified hummer’s method was utilized to prepare reduced-GO (Wang et al., 2018). Firstly, polymerization of NMA in the presence of an aqueous lignin solution is carried out to produce microspheres of lignin-PNMA. Secondly, the reduction-induced self-assembly method was incorporated to encapsulate the already prepared GO hydrogel nanosheets.

Nasrollahzadeh et al., (2014) reported the synthesis of GO/ZnO nanocomposites. Various tetrazoles were reported to be manufactured using this heterogeneous catalyst. Characterization was done by XRD, SEM, EDS, TEM, and UV-Vis spectroscopies. The employed method gave a high yield and provided simplicity in operation.

Depending on the desired use, graphene can be produced in following ways:

• Mechanical exfoliation (ME)

Each approach has various advantages and limits. The following is a quick summary of various strategies.

Due to its outstanding material characteristics, such as toughness, yield strength, and thermal conductivity, polymer nanocomposite (PNC) manufacturing has increased significantly over the last decade (Chang et al., 2013; Arash et al., 2014; Mahmood et al., 2014; Li and Wang, 2016; Wang and Zhi, 2016; Zope et al., 2016; Wang et al., 2019b; Khatun et al., 2020; Ibrahim et al., 2021). Traditional composite structures typically have a high proportion of filler bound (Nguyen and Nguyen, 2016). However, it has been demonstrated that micron-sized graphene can be scaled up for mass production. As a result, graphene-based composite materials are appropriate for a wide variety of applications (Lerner et al., 2014).

Significant amounts of graphene can be created by oxidizing graphite precursors. As a result, graphene-based polymer nanocomposites have piqued researchers’ curiosity all over the world. Graphene polymer nanocomposites have been made using a variety of polymers as matrices, including epoxy (Gervasoni, 2016; Wu et al., 2020a), PMMA (Ramanathan et al., 2007; Jang et al., 2009), HDPE (Zheng et al., 2004), polystyrene (Yuan et al., 2009), and nylon (Frontini and Pouzada, 2011; Goodfellow et al., 2016; Moore et al., 2018; Pierson et al., 2019; Qi et al., 2019; Schwarzer et al., 2019; Lin et al., 2022). The use of graphene-supported nanocomposites for the development of novel materials is becoming more common.

Nanocomposites have matrices consisting of polymer, metal, or ceramic, as well as fillers like nanotubes (CNTs), clay minerals, and graphene nanodots. These inclusions improve the material’s properties (Haraguchi, 2011; Mittal et al., 2015). Polymeric nanocomposites are the most versatile, with applications in a wide range of industries including energy, electronics, biomedical, and others. Polymer composites are separated into three groups based on the dispersion of nano-scale layers: intercalated nanocomposites, micro-composites, and exfoliated nanocomposites (Costantino et al., 2012).

Chemically, the compatible nature of the matrix and filler material, the concentration of the filler, and the dispersion of the filler impart different properties to graphene nanocomposites. Appropriate fabrication procedures must be used to produce the best outcomes. Furthermore, the degree of dispersion has a substantial relationship with the performance quality of nanocomposites (Frontini and Pouzada, 2011; Unalan et al., 2014; Fu et al., 2019). In situ polymerization, exfoliation adsorption, and melt intercalation are the three types of polymer nanocomposites.

Recent advanced research in the chemical and material science field has highlighted the usefulness of applying various kinds of nano-materials to enhance the structural integrity and strength of compounds. Among these, graphene-based nano-materials have received great attention. Research has shown that an addition of about 0.05% graphene in concrete materials has a significant impact on the enhancement of their mechanical properties (Isfahani et al., 2016). For better understanding, binding and formation analyses of nanocomposites, image analysis of the compound is an effective process. The data gathered can then be used to improve the predictions and designs for new nanocomposite materials. In this regard, Scrivener (2004) utilized the back-scattered electron (BSE) method to visualize the structure of such compounds at the 100 nm level. It also gave crucial information regarding various size factors and presented a fractal analysis for such nanocomposites (Ruan et al., 2018). However, this approach often faced technical difficulties in observing the full-scale impact of nano-materials on the structural enhancement at the micro level. In this regard, modern artificial intelligence (AI) approaches are now providing a new view regarding the evaluation of nanocomposites. Lin et al. (2022) has used convolutional neural networks (CNN), an advanced AI approach, to evaluate the mechanism of nanomaterial reinforcements using data image processing. Conventional approaches involving such studies often required a higher degree of technicality and sophistication, making the entire process more time-consuming and laborious. In comparison to this, using an AI approach eliminates such complexities as programmed algorithms are able to analyze and learn from a given dataset and ultimately perform wide-scale image analyses and extract important findings from the image study to better understand nanocomposites (Goodfellow et al., 2016). Furthermore, these AI methods exhibit their superiority in mining features regarding spatial correlation and other dominant factors in material microstructures. Ultimately, this provides a pathway for the microstructural characterization of nano-reinforced composite materials.

The use of AI models to assess nanocomposite structures has resulted in significantly higher levels of accuracy, and the overall predictive accuracy increases with the size of the dataset provided to the program. The increase in the image dataset provided more information for the program to study and enabled it to capture and analyze information regarding the material. At a micro level, the AI extracts higher-dimensional information such as porosity, solid material shape, and the arrangement of numerous phases, providing a better understanding of its structural dynamics. This enables a better interpretation of the potential mechanism behind nano-reinforcement of composite materials, aiding the design of such nanocomposites (Lin et al., 2022).

The unique features of AI, which tend to reduce the overall computational time for analysis and preparation of high-fidelity modeling of materials, make it an effective tool in material sciences (Qi et al., 2019). Cracking or fracturing is an important phenomenon occurring in different materials and is caused by the breakage of bonds at an atomic level. Understanding the behavior of cracking in materials is important from a scientific as well as industrial point of view in order to manipulate the integrity and durability of materials. In this regard, the development of such AI models that can predict the cracking phenomenon of materials based on the atomic level bond breaking data can provide numerous possibilities during nano-scale material designing. Various AI models have been developed to detect and study cracks in materials. Moore et al. (2018) developed a random forest-based advanced AI model using data from a finite–discrete element mode in order to predict vibrant fractures in materials (Moore et al., 2018). Similarly, certain models have been developed for the detection of microscopic 3D crack structures in brittle materials and alloys at microscopic levels (Pierson et al., 2019; Schwarzer et al., 2019). Furthermore, using the data obtained from molecular dynamics, a model was constructed to predict brittle fractures in general, with another related model developed for fracture mechanism prediction in graphene as a function of its crystal orientation (Lew et al., 2021).

Elapolu et al. (2022) developed an AI model for the prediction of brittle fractures occurring in polycrystalline graphene under certain tensile stress. The model uses convolution neural networks (CNN) to predict crack growth in realistic polycrystalline graphene by processing spatial and sequential datasets. The recorded spatial features included grain boundaries as well as their orientation, whereas their sequential features were linked with crack growth. Novel image processing data were collected and fed in to the model which carried out the predictive analysis. The final results obtained via AI model analysis depict a close agreement with the molecular dynamic simulation, which is used for obtaining information related to the fracture process in pre-cracked polycrystalline graphene sheets subjected to tensile load. The corresponding model gave accurate predictions regarding the crack path of graphene with high accuracy. It also provides instantaneous results, providing an option to bypass high-cost computational dynamic simulations. These AI-based modelings are now proving to be an effective method for a quick structural evaluation and can be a vital tool in graphene-based product developments.

Various analyses may be used to determine the physicochemical features of graphene-based nanocomposites, e.g., particle size and size distribution, shape, valence state, specific surface area, etc. As shown in Table 1, there are different techniques that can be used. As shown previously, graphene and its composites have a wide range of unique properties, making them ideal candidates for use in clean energy materials to reduce pollution and promote humanity’s long-term development.

Global industrialization has played a critical part in the modern world’s economic growth and urbanization (Hu and Teng, 2007; Sur, 2012; Wu and Hung, 2015). The expansion of a large number of enterprises resulted in pollution, mostly water contamination, as industrial waste was dumped into the aquatic environment (Karthik et al., 2015; Kumar et al., 2016; Latha et al., 2016). As a result of the wastewater generated by industries, major difficulties have risen in aquatic living areas. Preventing industrial wastewater from mixing into the aquatic environment is a difficult undertaking, and it is preferable to minimize the effluents before they are mixed into an aquatic environment. For the degradation of pollutants generated by companies, several technologies are available (Luo and Yang, 2017; Wang et al., 2019b; Longxing et al., 2019; Zhao et al., 2019). Photocatalysis is one of them, and it has the ability to degrade/decompose wastewater–contaminated water, primarily organic contaminants (Zhang et al., 2012b; Xiang et al., 2012). A range of metal composites and carbon-based materials have been used since the introduction of photocatalysis employing semiconductor nanoparticles (Liu et al., 2017a; Zhang et al., 2017a; Zheng et al., 2019), extending the applicability of graphene to the photocatalysis processes by tuning its electronic characteristics by inserting heteroatoms or functionality (Liu et al., 2011). As a result, the photocatalytic behavior of graphene-based nanomaterials is studied in this section.

Babu et al. (2015) used nanostructured rGO-supported CuO–TiO₂ for the photocatalyzed removal of methyl orange (MO). A modified Hummer’s method was used to make graphitic oxide from graphite powder. Sono-chemical techniques were utilized in preparing ex-foliateded graphene and reduced graphene oxide (rGO). Under ultrasonication, rGO is coated with a CuO–TiO₂ nanocomposite was also loaded onto the surface of rGO. The authors went over to determine the photocatalytic activity of rGO. The authors compare the photocatalytic performance of several photocatalysts, including CuO/TiO₂, CuO, TiO₂, and rGO-loaded CuO/TiO₂ nanocomposites. It was assumed that rGO-loaded CuO/TiO₂ had a higher photocatalytic proficiency than other photocatalysts. They also tested the photocatalytic response in a variety of settings, including darkness, ultrasound, light, diffused sunlight, and a combination of both visible and ultrasound. According to the findings, combining ultrasound with diffused sunlight resulted in better productivity. Monsef et al. (2021) tested carbon nano-composites in lanthanide oxides showing a high degree of proficiency for electrochemical activities and are more super capacitive than either of these oxides alone.

Liu et al. (2017a) have reported the photocatalytic degradation of phenol using the composites of TiO₂–G and ZnO–G. It was examined using SEM and TEM that both ZnO and TiO₂ were properly imparted on a graphene surface. TEM images were also used to assess the size of these nanoparticles. When compared to pure TiO₂ and ZnO nanoparticles, the rate was raised by up to 30 percent. This showed that graphene conducts electrons and suppresses recombination, while linking two composites created a more effective charge separation and extended charge carrier life, which improved the photocatalytic efficiency. Experimentation was optimized by controlling pH, intensity of light, phenol concentration, and catalyst dose. Using coupled ZnO–G/TiO₂–G photocatalysts, the full degradation of a 40-ppm phenol solution took just 60 min at the optimum conditions. The fact that graphene boosts the photocatalytic performance of semiconductors is well demonstrated by these examples in the literature.

Bhunia and Jana, (2014) developed a straightforward method for synthesizing nanocomposites of rGO with a loading of silver (rGO–Ag), which showed a high removal of atrazine, phenol, and A-bisphenol in visible light. The creation of pure rGO–Ag was firmly shown by XPS and XRD studies, as well as greater crystallinity. Furthermore, the authors used TEM analysis to establish the composite nature of rGO–Ag, demonstrating the homogeneous decorating of Ag nanoparticles over the rGO surface. Furthermore, Ag nanoparticles were calculated to have an average particle size of roughly 5 nm. Experimenters experimented with different catalyst loadings e.g., 1: ½ and 1: ¼. Furthermore, the authors carried out a reusability test and, using photoluminescence spectroscopy, proposed a mechanism for photocatalytic degradation.

Heavy metal contamination is an intensified environmental and aquatic system problem in today’s industrial world. The flow of these pollutants into the soil, water systems, and atmosphere severely pollutes ecosystems. Heavy metal ions are soluble in aquatic environments and also tend to bond with important biological constituents (i.e. proteins and nucleic acids), posing a danger to human health directly or indirectly (Jin et al., 2014). The mining sector, sewage irrigation, metal plating, electronic industry, pesticides, plastics, and fertilizers are all major sources of heavy metals (Ebrahimi et al., 2013; Maleki et al., 2015; Li et al., 2019; Saljnikov et al., 2019). Because of the increased use of heavy metals in businesses, the amount of heavy metals released into the ecosystem has recently increased (Zhong et al., 2016). Arsenic (As), cadmium (Cd), copper (Cu), chromium (Cr), lead (Pb), nickel (Ni), Mercury (Hg), and zinc (Zn) are the most frequent heavy metals found in the environment. These pollutants can cause major health problems in humans, including emphysema, kidney damage, hypertension, and cancer, even at low concentrations (Jacob et al., 2000; Cao et al., 2012; Gao et al., 2017).

Detection and elimination of these heavy metal ions from aquatic systems is important as even a trace quantity of these contaminants can cause major health problems (Motahari et al., 2015). Heavy metals are removed using a variety of technologies, including ion exchange, coagulation, reverse osmosis (RO), solvent extraction, and adsorption (Lesmana et al., 2009; Bashir et al., 2019). Among these, the adsorption approach is the most effective for removing heavy metal ions from aquatic systems.

There are numerous adsorbents in the market today, including activated carbon (Moreno-Fernandez et al., 2017), polymeric adsorbents, clay (Cantuaria et al., 2016), and palm shell (Ismaiel et al., 2013). However, the task is made more difficult by the limited performance against metal ion removal. Due to its excellent properties, e.g. greater surface area, graphene and its oxide derivatives have been efficaciously employed as adsorbents (Chowdhury and Balasubramanian, 2014; Cortés-Arriagada and Toro-Labbé, 2016). Graphene oxide (GO) has also been reported as a versatile adsorbent for the removal of heavy metal ions e.g., Au(III) and Pt (IV) (Liu et al., 2012), Pb(II) (Huang et al., 2011), Cu(II) (Yang et al., 2010), Zn(II) (Niu et al., 2013), Cd(II) (Bian et al., 2015), and Co(II) (Gao et al., 2011). Due to aggregation, Pure GO has a huge surface area, making it ineffective for heavy metal removal. As a result, the chemical treatment of GO surface was necessary to remove the heavy metal ion (Cao and Li, 2014).

Membranes’ inability to withstand a wide range of contaminants is a significant unsolved problem in water treatment. Recent improvements have been seen in water purification through membrane technology by constructing modified membranes with graphene films (Garaj et al., 2010; Surwade et al., 2015; Abraham et al., 2017). On the other hand, these techniques include a succession of highly concentrated and controlled, resource-conserving, sophisticated operations that are challenging to perform uniformly at greater densities. As a result, CVD graphene sheets’ potential to purify and desalinate water is limited to small-scale experiments (Surwade et al., 2015). Furthermore, while CVD synthesis allows for the precise control of graphene sheet formation, in addition, the hydrophobic characteristics of CVD-G materials typically cause further barriers to their use in water purification membranes. As a result, the economic practicability of these sheets for water filtration is hampered by these technical constraints (Sun et al., 2016).

MD is a new thermally driven water purification technology with a lot of promise for treating highly salty water especially seawater, such as reverse osmosis (Drioli et al., 2015). In the MD process, the pressure differential over a porous/hydrophobic membrane drives water purification. Because of these benefits, MD is a green technology in purification processes with zero liquid discharge (Tijing et al., 2015). MD has a few major limitations: a huge amount of energy requirement, temperature maintenance, and the MD membrane’s inability to tolerate a wide range of pollutant combinations (Drioli et al., 2015; Tijing et al., 2015). Furthermore, conduction in typical MD membranes results in poor water vapor flow and performance degradation over time, which remains a serious concern (Camacho et al., 2013). For antifouling membranes to properly handle these difficulties, new materials for antifouling membranes are required. Several approaches, such as electrospinning and phase, have been used to create improved MD membranes. However, getting large amounts of filtered water without fouling the membrane is still a problem (Lin et al., 2014; Boo et al., 2016). The use of graphene flakes in membranes has recently been shown to improve the efficacy of water purification processes (Woo et al., 2016). However, the vast potential and promises of 2D graphene films for water purification have yet to be fully employed.

Yuyang et al. (Wang et al., 2021) used an AI approach to synthesize G-based membranes for water purification. Because of their exceptional physical properties, 2-D nanomaterials, mainly graphene, have been intensively explored. The nanopore morphology and topology of such materials can have a big impact on how well they work in real-world problems. However, finding the most efficient nanopores frequently necessitates expensive and time-consuming experimentations. A study suggested a data-driven artificial intelligence framework for determining the most efficient graphene nanopore for its application in water treatment. When compared to typical circular nanopores, the MD simulations of prospective AI-created graphene nanopores show a high ion rejection rate with higher water flux as compared to other membranes. The irregular shape of AI-created pores with rough edges has been found to be a key factor in their high water desalination performance. Finally, this study shows that artificial intelligence can be a useful tool for creating and screening nanomaterials.

According to Dong et al. (Seo et al., 2018), the samples were then wet transferred to a PTFE/MD membrane. Unlike traditional CVD procedures, ambient-air graphene manufacturing does not necessitate the use of costly and potentially explosive purified compressed gases (Ruan et al., 2011). Growth source is replaced with soybean oil, which is a cheap, safe, and sustainable bio-agent. On a polycrystalline-Ni substrate, ambient-air CVD technique allows the formation of continuous graphene sheets that are suitable as water vapor-permeable channels. The PTFE membrane is used as supporting component. At the same time, the membrane can reject both salt and hazardous water-borne contaminants including surfactants and oils. Real seawater is processed through the developed membrane to demonstrate its practical applicability under real desalination circumstances. A commercial PTFE-based MD membrane was fouled during the processing of saltwater, causing a continuous decline in the water vapor flux and a small decrease in salt rejection over 72 h. A permeable graphene-based membrane, on the other hand, demonstrated 100 percent salt rejection while maintaining a high water flow and long-term stability over 72 h. Long-term resilience of permeable graphene-based membrane under actual seawater feed was also established. This study demonstrated water desalination using a graphene membrane containing nanochannels of multilayers. Graphene-based membranes had much greater water vapor flux retention and salt rejection rates than the standard distillation membranes, as well as a superior antifouling performance under a mixture of saline water containing pollutants such as oils and surfactants.

For hydrogen fuel cells, water splitting is one of the basic units that are used for hydrogen production which takes place according to the following reactions (Ali et al., 2021): 4 H + + 4 e - → 2 H  2 E =( 0-0 .059   pH ) V (1) 2 H 2 O → O 2 + 4 H + + 4 e - E =( 1 .23   V-0 .059pH ) V (2) Overall:   2 H 2 O → 2 H 2 + O 2 ΔE=-1 .23   V (3)

For the production of electricity directly from the chemical energy of oxygen and hydrogen, HFCs are more efficient power generation devices from which more than 50% of power can be generated efficiently (Durbin and Malardier-Jugroot, 2012). Reverse reaction of electrolyzing water is the working principle of HFCs. See Equations 1–3. Through the catalyst, hydrogen ions are generated at the anode by hydrogen when oxygen and hydrogen are supplied at the cathode and anode, respectively, from which, lost electrons generate electricity through an external circuit when they reach the cathode. On the other hand, hydrogen ions on the anode side through a polymer electrolyte membrane (PEM) reach the cathode side to generate water after the reaction with oxygen. High power, long driving distance, no noise, low emissions of pollutants, and an environmentally friendly behavior are some of the advantages of the HFCs (Xin et al., 2019). Table 2 summarizes the different catalytic reactions involved in the energy production through fuel cell.

In a study about the fabrication of GO/Nafion composite membrane, Choi et al. (Choi and Lee, 2012) explained methanol permeability, which is greatly reduced by the addition of GO as a filler. The reason for the modification of the microstructure of hydrophilic and hydrophobic PEM regions is the interaction of GO (hydrophilic nature) with non-polar main chain and cluster of polar ions. Thus, in the presence of water, phase separation of the membrane is reduced while it is improved in the case of permeability of proton exchange membrane.

In a study by Hu et al. (2014), they reported the characteristics of graphene-based membrane materials for the transportation of protons and found that good conducting membranes for protons, graphene, and boron nanocomposites could be used, which are two-dimensional and consist of a single layer of atoms.

Including possible solutions, flexibility, low cost, light weight, and large area coatings are all the considerable advantages that have gained interest regarding solar cells which are based on bulk heterojunction (BHJ) (Günes et al., 2007; Li et al., 2012). BHJ absorbs solar light efficiently because it consists of a mixture of polymer materials that are electron donors and acceptors, which dissociate into free charge carriers while generating excitons (van Hal et al., 2003; Xiao et al., 2018; Marforio et al., 2019).

In PSCs, photoactive blends are the most widely used acceptors that are based on poly (3,4-ethylenedioxythiophene) (P3HT) and fullerene. To increase efficiency, recently, polymers with low bandgaps have been developed which are able to absorb a wide range of solar spectra (Peet et al., 2007; Ma et al., 2015). However, commercialization of PSCs is greatly affected by many factors, which include chemical instability, low efficiency of power conversion, fast degradation of photoactive layers, and an insufficient separation of charges (Lipomi et al., 2012; Savagatrup et al., 2015).

Because of its unusual physicochemical properties and numerous applications in fuel cells, solar cells, sensors, batteries, and photocatalysis, graphene sheets with a two-dimensional structure have piqued the interest of researchers (Stoller et al., 2008; Loh et al., 2010; Chen et al., 2013; Mahmood et al., 2014; Liu et al., 2017b; Abdullah and Hashim, 2019). For PSC applications, graphene proves to be an inspiring candidate because of its tunable work function, optical transparency, and outstanding electrical conductivity (Eletskii et al., 2011; Seo et al., 2018).

Different components in PSCs have been presented in recent research studies, which are mainly based on the utilization of graphene and graphene/nanocomposites (Liu et al., 2014). Graphene offers a wide range of opportunities because of its active edges and large surface area to develop a highly efficient PSC while incorporating various polymeric, organic, and inorganic materials (Vinoth et al., 2017).

A successful implication of graphene oxide (GO) was carried out by Li et al. (2010) with electron donor polymers and an anode as a hole-transporting layer (HTL) due to its proper function with it. Furthermore, researchers motivated the work based on the GO buffer layers in PSC. The work function of poly (3-hexylthiophene) [P3HT] closely matches the calculated work function of GO (−4.7 eV), facilitating the hole transport from the donor polymer (P3HT) to anode. A suitable work function and an effective separation of charges mainly enhance the performance of GO sheets. Likewise, GO was represented as a HTL in a PSC inverted-type device with the architecture of ITO/ZnO/C60-SAM/P3HT: PCBM (Gao et al., 2010).

In PSC, graphene sheets have been widely used, which are few-layered or single in nature, transparent and flexible (He et al., 2012). Graphene with the device structure of CVD-grown graphene/PEDOT: PSS/CuPc/C60/Al was used as an anode in PSC (Gomez De Arco et al., 2010). Maximum PCE displayed by the electrode of the resultant CVD-grown graphene was comparable with devices such as the PCE of ITO-based PSC. In PSC-inverted devices, a top electrode was demonstrated by CVD-grown graphene in earlier research studies (Lee et al., 2011). Remarkably, as compared to the widely used ITO anode, CVD-grown graphene exhibited better mechanical stability and also increased the PCE.

On the other hand, the bottom electrode was constructed in PSC based on P3HT: PCBM on the basis of chemically reduced graphene (rGO). The polyethylene terephthalate (PET) substrate, which is flexible, was spin-coated with rGO sheets. Under the optimized rGO thick films, almost 0.78% of the highest PCE was achieved. A graphene as the electrodes for both the anode and cathode for flexible PSC has been designed by Park et al. (2014). PSC devices significantly enhanced the PCE from 6.1% to 7.1%, which is based on the graphene anode and cathode, respectively. Recently, for PSC, a possible solution was reported by Zhang et al. rGO/silver nanowires (AgNWs)/rGO, which is based on the transparent electrode (Zhang et al., 2017b). At 550 nm, a 90% transparency was shown by the hybrid electrode rGO/AgNWs/rGO. As compared to control devices that are fabricated with an ITO electrode, the electrode rGO/AgNWs/rGO demonstrated excellent mechanical flexibility and improved photovoltaic performance.

The agriculture sector is of great importance as it provides a great deal of the materials necessary to ensure food, feed, and fiber for human life. Satisfying these basic needs of mankind in the face of climate change and rising global population is a major challenge. In order to combat such issues, an integrated technological approach is necessary to ensure global food security. Since the last decade, the discovery of graphene materials has resulted in the advancement in various fields of life, including the field of agriculture.