Enhanced rGO/ZnO/Chitosan Nanozyme Photocatalytic Technology for Efficient Degradation of Diazinon Pesticide Contaminated Water

doi:10.21203/rs.3.rs-5255206/v1

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Enhanced rGO/ZnO/Chitosan Nanozyme Photocatalytic Technology for Efficient Degradation of Diazinon Pesticide Contaminated Water

https://doi.org/10.21203/rs.3.rs-5255206/v1

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published 16 Dec, 2024

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The increasing presence of pesticide contaminants in water bodies poses significant environmental and health challenges. This study introduces a novel enzyme-based photocatalytic technology composed of reduced graphene oxide (rGO), zinc oxide (ZnO), and chitosan (CS) designed to enhance the degradation efficiency of diazinon pesticides in polluted water. The nanozymes were characterized by XRD, SEM-EDX, and FTIR to ensure homogeneous structure and distribution of the materials, and the adsorbed pesticide content was measured using a UV-Vis spectrophotometer. Adsorption studies showed that the diazinon removal efficiency increased with higher pH, longer contact time, and initial concentration, reaching maximum adsorption efficiency at neutral pH. Isotherm analysis showed that diazinon adsorption on rGO/ZnO/CS nanozymes followed the Freundlich model, exhibiting heterogeneous adsorption characteristics with moderate adsorption capacity. These findings highlight the potential of rGO/ZnO/CS nanozymes as effective adsorbents for removing diazinon pesticides from contaminated water, offering promising applications in environmental remediation.

degradation

diazinon

nanozyme

photocatalytic

The increase in pesticide use in the agricultural sector has significantly impacted environmental quality, especially on water resources (Teklu et al., 2022; Tudi et al., 2021). Pesticide residues that pollute water bodies harm aquatic ecosystems and pose health risks to humans (Ali et al., 2021; Pawan Kumar et al., 2023). Therefore, developing effective and efficient technology for the degradation of pesticide contaminants in water is a top priority in efforts to mitigate environmental pollution (Jatoi et al., 2021; Rajmohan et al., 2020). Various methods have been designed to deal with pesticide contamination, from physical to chemical to biological processes (Ahmed et al., 2021; Hassaan & El Nemr, 2020; Saleh et al., 2020). However, conventional techniques are often less effective, expensive and cause unwanted side products.

Diazinon is an organophosphate pesticide widely used in agriculture to control insect pests on various crops. Although effective in protecting crops, excessive and uncontrolled use of diazinon has raised serious concerns regarding environmental and human health impacts (Al-Otaibi et al., 2019; Mena et al., 2020; X. Wu et al., 2021). Diazinon contamination in surface and groundwater can cause poisoning in aquatic organisms and land animals and potentially enter the human food chain (Glinski et al., 2018; Hajirezaee et al., 2017). The persistent and toxic nature of diazinon makes it challenging to remove from the environment using conventional methods (Barjasteh-Askari et al., 2022a; Dehghani et al., 2024; X. Wu et al., 2021). Therefore, an effective and efficient innovative technology is needed to degrade diazinon in water (Bikash Baruah, 2022; Zhu et al., 2023). One promising approach is using nanozymes, namely nanomaterials with a catalytic activity that resembles natural enzymes.

Enzymes play an essential role in environmental monitoring and remediation, but the fragile performance of naturally occurring enzymes significantly limits their broader use in complex matrices (Al-Sakkaf et al., 2023; Sanjay Parethe et al., 2022; S. K. Singh et al., 2021). In environmental monitoring and remediation, nanozymes have emerged as a powerful alternative to natural enzymes, thanks to their catalytic capabilities that withstand harsh environmental conditions (Ballesteros et al., 2021; Mishra et al., 2022; J. Wu et al., 2019). The advantages of nanozymes in catalytic signal amplification enable them to detect various contaminants effectively (Ballesteros et al., 2021; Diao et al., 2024; Su et al., 2022). In contrast, their superior catalytic properties provide great potential for the degradation of pollutants (Ballesteros et al., 2021; Diao et al., 2024). Recent research highlights significant advances in using nanozymes to detect and remove environmental pollutants, demonstrating the great potential of this technology in more efficient pollution control and environmental protection (Ballesteros et al., 2021; Mishra et al., 2022). This critical study examines various applications of nanozymes in detecting contaminants such as toxic ions, phenolic pollutants, antibiotics, and pesticides and summarizes degradation strategies involving nanozymes (Diao et al., 2024; Wei et al., 2019; Wong et al., 2021; X. Zhang et al., 2019). Additionally, opportunities and challenges in further developing nanozyme catalysis are presented to guide future efforts in this field.

Nanozymes, while functioning similarly to natural enzymes, exhibit distinct structural differences. Unlike natural enzymes, which feature uniquely designed catalytic sites optimized for catalysis, nanozymes are often synthesized through random processes, leading to lower catalytic efficiencies than their natural counterparts (R. Singh et al., 2023; Y. Zhang et al., 2021). Efforts in research aim to enhance nanozymes to achieve catalytic capabilities that match or surpass those of natural enzymes. This requires a comprehensive exploration of their structural characteristics and relationship to catalytic activity compared to natural enzymes. Integrating the catalytic mechanisms found in natural enzymes into the rational design of nanozymes can significantly improve production strategies (Huang et al., 2019; Sheng et al., 2024). By employing bioinspired approaches, recent advancements have led to the development nanozymes that exhibit catalytic activities that approach or exceed those of natural enzymes. As a result, pursuing highly active, bioinspired, and carefully engineered nanozymes has emerged as a prominent area of interest, paving the way for various strategies to enhance catalytic performance and specificity (Y. Zhang et al., 2021).

Enzyme-mimicking nanomaterials encompass metals, metal oxides, or carbon-based materials, exhibiting catalytic behaviours akin to peroxidase, superoxide dismutase (SOD), catalase, or oxidase. The catalytic efficacy of nanozymes hinges on factors such as atomic composition, size, morphology, solution pH, surface coating, and surface chemistry of the nanomaterials (Adeyemi et al., 2018; Navya & Daima, 2016). Among these crucial physicochemical attributes, the composition, surface chemistry, and surface modifications of enzyme-mimicking materials significantly influence their inherent nanozyme activity (Huang et al., 2019; J. Wu et al., 2019). The selection of appropriate materials is crucial for designing complex nanozymes that exhibit photocatalytic properties in degrading contaminants. Photocatalytic nanoparticles such as zinc oxide (ZnO) and graphene are extensively utilized today. ZnO is highly valued for its direct bandgap energy, binding energy of 60 MeV, stability against chemical corrosion, non-toxic nature, insolubility, ability to degrade toxic organic compounds, capability to absorb various electromagnetic waves, and efficient photocatalytic properties. It is recognized as a critical semiconductor material (Handayani et al., 2022; Nisar et al., 2022). Recent research has highlighted its effectiveness in the photocatalytic degradation of diazinon from aqueous solutions (Jonidi-Jafari et al., 2015; Maleki et al., 2020). Graphene oxide (GO), known for its extensive surface area and exceptional photocatalytic abilities, maintains these traits when reduced to reduced graphene oxide (rGO), making it a potent catalyst for diazinon degradation (Dehghan et al., 2022; Mohammadi et al., 2023). The electrons in rGO's conduction band play a crucial role in generating hydroxyl radicals (⋅OH), which are instrumental in mineralizing organic substances (Afreen et al., 2020; Byzynski et al., 2018). ZnO enhances this process by also contributing to the generation of ⋅OH radicals (Sultana et al., 2020). The rGO and ZnO can augment the enzymatic activity involved in diazinon degradation. For instance, the ⋅OH radicals produced during photocatalysis can activate enzymes that break down diazinon into less harmful compounds (Barjasteh-Askari et al., 2022b; Salehzadeh et al., 2024).

Recent studies have shown that achieving stability in nanozymes can be effectively accomplished by applying diverse coating materials, including small organic molecules, polymers, and inorganic substances (Ballesteros et al., 2021). Specifically, research highlights chitosan (CS) as a promising coating material capable of enhancing magnetic nanoparticle stability and catalytic efficiency (Liu, Huang, et al., 2024; Nikoshvili et al., 2023). The synergistic combination of rGO, ZnO, and CS in nanozymes offers a promising solution for pesticide degradation (Handayani et al., 2022; Nisar et al., 2022). These nanozymes capitalize on rGO's high surface area and adsorption capabilities, ZnO's robust photocatalytic properties, and CS's biocompatibility and stability. Coating these materials enhances their strength and catalytic performance, which is crucial for the efficient degradation of pesticides like diazinon. Further advancements in structural and surface engineering can optimize the exposure of active catalytic sites, thereby boosting degradation efficiency. This innovative rGO/ZnO/CS nanozyme holds excellent potential for various environmental applications, including the detection and decomposition of pesticide contaminants, thanks to its enhanced photocatalytic activity, electron transfer mechanisms, and enzymatic capabilities.

2.1 Materials

The materials used in this study were chitosan (DD > 95%) and graphite from Sigma-Aldrich (St. Louis, MI, USA), sodium hydroxide, ethanol, acetic acid, urea, hydrochloric acid each from Merck (Darmstadt, Germany), organophosphate pesticide diazinon from Sigma-Aldrich (St. Louis, MI, USA), and deionized water was used for all reaction processes.

2.2 Instruments

Functional group analysis of rGO and nanozyme rGO/ZnO/CS was performed using Fourier Transform Infrared Spectroscopy (FTIR) with a Shimadzu IRPrestige-21 spectrometer, covering wavelengths from 500 to 4000 cm⁻¹. The crystal structure of the samples was determined by X-ray diffraction (XRD) using a Bruker D2-phaser X-ray diffractometer with Cu Kα radiation (λ = 1.5404 Å) operated at 40 kV and 30 mA. Based on the XRD data, the Scherrer equation estimated particle sizes of rGO and nanozyme rGO/ZnO/CS. Further characterization of the size, shape, and elemental composition (weight percentage) of nanozyme rGO/ZnO/CS was confirmed using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) from Thermo Scientific Quattro operating at 20 kV. The BET method was used to evaluate the specific surface area of the samples, while the pore size and pore volume distribution were measured using the BJH method with the Quantachrome Autosorb-iQ instrument.

2.3 Synthesis of Reduced Graphene Oxide (rGO)

Firstly, graphene oxide (GO) is synthesized by the oxidation of graphite using a method like the modified Hummers' method, resulting in a dispersion of GO in water. The GO solution was ultrasonicated at 53 kHz for 4 hours. The GO dispersion is then mixed with urea, which acts as a reducing agent and a nitrogen source for doping. This mixture is subjected to hydrothermal treatment at temperatures ranging from 150 to 200°C for several hours. During this process, urea facilitates the reduction of GO by removing oxygen-containing functional groups and simultaneously incorporating nitrogen into the graphene structure. After the reaction, the product is washed with water and ethanol to remove residual urea and by-products, followed by drying to obtain the final rGO material.

2.4 Synthesis of rGO/ZnO/CS Nanozyme

Synthesis of rGO/ZnO/CS nanozyme, shown in scheme 1. The ZnO solution at 10 mg mL⁻¹ in 98% ethanol solvent was added gradually to the rGO dispersed in deionized water at 5 mg mL⁻¹. The mixture was homogenized and stirred for 30 minutes, then transferred to a hydrothermal Teflon tube for oven treatment at 150°C for 120 minutes. After cooling to room temperature, the mixture was filtered through a 0.45 µm Whatman filter paper washed with ethanol and deionized water. The obtained residue was dried for 24 hours in an oven at 100°C and stored at room temperature.

A CS solution was prepared at a concentration of 2% by dissolving CS in a 2% acetic acid solution. Once CS was fully dissolved, 0.5 mL distilled water, 1 mL ethanol, and 0.5 mL HCl were mixed thoroughly with 8 mL chitosan solution for 2 hours at 60°C. Then, a 0.1 M NaOH solution was added dropwise until a slightly basic solution was obtained and left to gel for 48 hours until complete gelation. The formed gel was washed with deionized water until it was neutral. The rGO/ZnO powder formed was added to the CS mixture. Then, 0.1 M NaOH solution was added dropwise until the pH reached 7 and stirred for 2 hours at 70°C. The resulting rGO/ZnO/CS mixture was washed three times with deionized water, dried at 80°C for 48 hours, and then stored in a glass bottle.

2.5 Measurement of diazinon pesticide standard solution

A standard solution of diazinon pesticide was prepared in varying concentrations of 5, 10, 20, 30, 40, and 50 mg L⁻¹, dissolved in a 1:1 water and ethanol 98% solution. The maximum wavelength was determined using a UV-Vis spectrophotometer on a 50 mg L⁻¹ diazinon solution. The wavelength range used was from 200–300 nm. The obtained maximum wavelength (λ_max) was used to measure the absorbance of the standard concentrations and test samples. The absorbance of the standard solutions was measured using the UV-Vis spectrophotometer at the obtained λ_max. A standard curve was then created to plot the relationship between absorbance and pesticide concentration. This resulted in the equation y = ax + b, where a is the slope and b is the intercept.

2.6 Measurement of degradation activity in the diazinon pesticide

The degradation process was conducted based on the effects of pH (6; 6.5; 7; 7.5 and 8), contact time (20, 40, 60, 80, and 100 minutes), and pesticide concentration (5, 10, 20, 30, 40, and 50 mg L⁻¹). A 30 mg L⁻¹ diazinon pesticide solution was stirred with rGO/ZnO/CS nanozyme dosage of 300 mg. This treatment was based on the optimal pH and contact time. The mixture was filtered, and the solution was measured using a UV-Vis spectrophotometer at a wavelength of 247 nm. The difference between the initial concentration and the non-adsorbed solution concentration determined the adsorbed diazinon pesticide solution concentration. According to previous procedures, the photocatalysis process with UV light assistance for diazinon pesticide degradation was performed in a UV lamp reactor. In all experiments, the UV lamp was placed at 15 cm from the samples. The residual diazinon was measured using a spectrophotometer (Hitachi U-2900/2910) at a wavelength of 247 nm. The obtained measurements were used to determine the adsorption capacity. The adsorption capacity was calculated using the adsorption isotherm equations, namely the Freundlich Isotherm and Langmuir Isotherm.

3.1 FTIR Characterization

The FTIR spectrum of graphite gives absorption spectra of O-H and C = C functional groups (Rochman et al., 2019). Then, the spectrum of GO gives absorption spectra of O-H, C = C, C = O, C-OH and C-O functional groups (Emiru & Ayele, 2017). 23. The absorption peaks at 1042 cm^− 1 (for C–O stretching), 1729 and 1734 cm^− 1 (for C = O stretching), 1165 and 1203 cm^− 1 (for O = C–O–C stretching), and a broad band around 3400 cm^− 1 (for linking –OH stretching) are attributed to hydroxyl, carbonyl, carboxyl, epoxy, and ester groups, respectively, confirming the presence of various oxygen-containing functional groups for GO (An et al., 2018). The FTIR spectrum of rGO shows the reduction of oxygen-containing groups on GO by urea (Fig. 1a(i)). The spectrum of rGO shows a band at 3435 cm^− 1 due to O-H stretching. The band at 1633 cm^− 1 appears due to C = C stretching. The peak at 1633 cm^− 1 in rGO shows a strong band, indicating the restoration of the sp² lattice (Kanta et al., 2017). This shows GO's loss of oxygen-containing functional groups (C-OH and C = O). This spectrum of rGO confirms that most of the oxygen-containing functional groups have been removed. However, some residual oxygen functions in GO are still present on the surface of rGO with weaker intensity after reduction. This indicates that the GO material has been successfully reduced by urea, and the results show the same spectrum characteristics as those of previous studies (Emiru & Ayele, 2017; Faniyi et al., 2019; Kanta et al., 2017).

Figure 1a(ii), the FTIR spectrum of rGO/ZnO/CS shows broadband at around 3448 cm-1, corresponding to the hydroxyl group. The FTIR of pure CS shows a prominent band in the range of 3000–3500 cm^− 1, corresponding to the N − H and O − H stretching vibrations. The 1624 cm-1 and 1340 cm^− 1 peaks are C = C and C–OH, respectively, indicating the presence of graphene sheets [45]. The prominent absorption peak at 1408 cm^− 1 can be attributed to the CH2 bending of CS groups. The peak at 1024 cm^− 1 exhibits the C − O and C-N stretching vibrations 27. In addition, the peak at 1024 cm^− 1 is related to the formation of Zn-O-C linkages between rGO and ZnO layers [46]. The peaks at 451 cm^− 1 and 551 cm^− 1 were assigned as the characteristic stretching vibrations of Zn-O [47]. The presence of ZnO and rGO spectral bands in the prepared ZnO/rGO confirmed the successful formation of rGO/ZnO/CS nanozymes.

3.2 XRD analysis

The XRD pattern for graphite powder is at 2θ = 26.4° with the corresponding layer-to-layer spacing of 0.34 nm, like the literature (Emiru & Ayele, 2017). GO material has a sharp diffraction peak at 2θ = 10.74° (hkl 001) and a small peak at 2θ = 42.42° (hkl 100) (Rochman et al., 2019). In addition, Emiru's study for GO is around 2θ = 10.9°, corresponding to the layer-to-layer spacing of 0.88 nm (Emiru & Ayele, 2017). The results showed that the interlayer spacing of GO is more significant than that of graphite due to the insertion of oxygen-containing functional groups between layers (Emiru & Ayele, 2017). XRD analysis confirmed rGO, as seen from Fig. 1b(i), the diffraction peak shifted to 2θ = 26.41° for the crystal plane of rGO (hkl 002) of the hexagonal structure. The interlayer distance of rGO can be estimated from the Bragg equation according to the layer-to-layer distance of 0.341 nm or 3.41 Å, which is by the interlayer distance of other studies, which is 3.47 Å (D’Oliveira et al., 2020). This indicates that when GO is reduced to rGO, the interlayer distance decreases due to the removal of oxygen-containing functional groups, resulting in the re-stacking of rGO sheets.

XRD identified synthetic ZnO powder at the clear diffraction peaks at 31.52°, 34.18°, 36.01°, 47.31°, 56.37°, 62.64°, 66.15°, 67.73°, and 68.86° corresponding to hkl (100), (002), (101), (102), (110), (103), (200), (112), and (201) planes of ZnO hexagonal prisms, respectively (JCPDSNo.36-1451) (Sengunthar et al., 2020). The characteristic diffraction peaks for graphene sheets in the XRD pattern of rGO/ZnO/CS, which may be at the (002) peak at 26.40°, confirm the presence of rGO in the nanozyme. The diffraction peaks at 44.06 and 64.43, being the typical 2θ peaks for ZnO, are shown in Fig. 2b(ii) (T. Wang et al., 2020; Y. Wang et al., 2018). The reduced diffraction peaks for ZnO indicate the change in the crystal structure or the influence of other materials, such as rGO and CS, in the rGO/ZnO/CS nanozyme. The incorporation of rGO and CS in the rGO/ZnO/CS nanozyme may cause changes in the ZnO structure, reduce the crystallite size, and more dispersed or aggregated distribution of ZnO in the nanozyme matrix, all of which contribute to the decrease in the intensity of the ZnO diffraction peaks. The presence of characteristic peaks for rGO and ZnO confirms the formation of complex nanozyme (Sengunthar et al., 2020; T. Wang et al., 2020; Y. Wang et al., 2018). The average grain size of ZnO nanoparticles in rGO/ZnO/CS nanozyme was also estimated using the Debye-Scherrer formula to be 18.99 nm.

3.3 Structural and morphological characterization

The morphology of rGO and rGO/ZnO/CS nanozyme is presented using scanning electron microscopy (SEM) in Fig. 2. Figure 2(a) illustrates the structure of rGO, which exhibits a layered morphology with a rough and irregular surface. This structure is characteristic of rGO, where individual graphene layers stack on top of one another, forming aggregates (Li et al., 2020; Ortiz-Aguayo et al., 2024). Such a morphology provides a large surface area, enhancing its electrical conductivity and capacity for catalysis and adsorption applications (Garcia-Bordejé et al., 2021; Smith et al., 2019). Additionally, this layered structure signifies a successful reduction process of graphene oxide, evidenced by the loss of most oxygen groups and an increase in the degree of crystallinity (Smith et al., 2019).

Figure 2(b) presents the morphology of the rGO/ZnO/chitosan (CS) nanozyme, a composite comprising rGO, ZnO nanoparticles, and chitosan. In this image, ZnO particles are observed scattered across the surface of rGO. The ZnO particles are spherical and exhibit varying sizes, with some appearing to be attached to the rGO surface. SEM analysis of ZnO reveals particle agglomeration (Zaman et al., 2022). This suggests that the SEM image of the nano-sized rGO/ZnO/CS nanozyme shows substantial accumulation along with a narrow size distribution. Figure 2(b) depicts that the spherical ZnO nanoparticles are distinct, uniform, and evenly dispersed throughout the rGO and chitosan matrix (Amanat et al., 2024; Liu, Xiao, et al., 2024; Sodeinde et al., 2022). The presence of ZnO particles on rGO boosts catalytic activity, while chitosan helps stabilize the structure and adds biocompatibility to the composite (Sharma et al., 2024; Sun et al., 2022). Integrating these three components is anticipated to produce nanozymes with enhanced activity and better stability for various applications, including biosensor detection and pollutant degradation (Amanat et al., 2024; Liu, Xiao, et al., 2024; M. Wang et al., 2023). The successful formation of the rGO/ZnO/CS composite resulted in the uniform distribution of ZnO particles across the rGO matrix. The layered architecture of rGO created an ideal platform for the dispersion of ZnO particles, while chitosan acted as an additive that enhanced biocompatibility. This morphological characterization is crucial for verifying that the composite structure has been effectively established, thereby supporting the material's potential applications in various domains, including catalysis, sensors, and environmental solutions.

3.4 Maximum Wavelength and Standard Curve

Figure 3a illustrates the correlation between wavelength (nm) and the absorbance of the diazinon pesticide. The spectrum reveals that diazinon's peak absorbance (λmax) occurs at a wavelength of 247 nm. This peak indicates diazinon compounds absorbing light at this specific wavelength, resulting from electronic transitions within their molecules, such as the chromophore group's π-π* or n-π* transitions. Figure 3b depicts the linear relationship between the concentration of diazinon (mg L^− 1) and the absorbance values obtained using a UV-Vis spectrophotometer. The calibration curve yields the linear equation y = 0.016x + 0.0056, where y represents the absorbance and x indicates the concentration in mg L^− 1. The coefficient of determination (R² = 0.9958) demonstrates a robust correlation between concentration and absorbance, with a value approaching 1, suggesting that nearly all experimental data align closely with the regression line.

The slope coefficient indicates that for every one ppm increase in concentration, the absorbance rises by 0.016 units. This result demonstrates how sensitive the spectrophotometric method is in detecting variations in diazinon concentration. The small intercept, close to zero, indicates that the absorbance value approaches zero as the diazinon concentration nears zero. This finding implies that measurements at low concentrations remain precise and do not experience significant interference from other components. This calibration curve is valid and reliable for quantitatively analysing diazinon pesticide concentrations ranging from 0 to 50 mg L^− 1. The derived linear equation is an effective model for determining pesticide concentrations in various environmental samples or agricultural products. Such analysis is crucial for monitoring pesticide residues to ensure they remain within safe limits.

3.5 Degradation Activity of the Diazinon Pesticide

Effect of pH. Photocatalytic degradation of pollutants in polluted water depends on the pH of the solution since pollutants have different pH values in aqueous solution. Figure 4a shows that the adsorption efficiency of diazinon on rGO/ZnO/CS nanozymes increases with pH, reaching a peak at around pH 7, followed by a decrease as the pH continues to increase. The removal efficiency of diazinon pesticides reaches 8.32% at pH 7, with the adsorption capacity of rGO/ZnO/CS nanozymes reaching 7.12 mg g^− 1. This behaviour is typical for adsorption, where the adsorbent's surface charge and the adsorbate's ionisation are pH dependent. At lower pH levels, the surface of the adsorbent may be positively charged, leading to weaker interactions with diazinon, which is likely in the neutral or negative form. Therefore, the adsorption efficiency is lower. Where the removal efficiency of diazinon pesticide at pH 6 and 6.5 were 3.18% and 5.76%, respectively, this could be the optimum pH at which the surface charge of the nanozyme and the ionisation state of diazinon support maximum interaction. In this case, the adsorbent's neutral or partially charged form can maximise the binding efficiency. At higher pH of 7.5 and 8, they respectively reached 7.89% and 5.18%, where the decrease in adsorption efficiency is most likely due to the formation of more negatively charged species from diazinon and the adsorbent surface, which repel each other, reducing adsorption.

Effect of contact time. Figure 4b illustrates a consistent increase in adsorption efficiency as contact time increases, peaking at approximately 100 minutes. This trend indicates that the adsorption process benefits from longer exposure durations, allowing diazinon molecules to interact with and adhere to the active sites on the nanozymes. During the initial 20 to 40 minutes, the adsorption rate is slow, likely due to initial resistance to mass transfer between the solution phase and the adsorbent surface. After 40 minutes, the adsorption rate accelerates as more diazinon molecules diffuse to the nanozyme surface, occupying the available adsorption sites. At 100 minutes, adsorption reaches equilibrium, with all active sites on the adsorbent filled, resulting in no significant increase in adsorption efficiency; at this point, the adsorption efficiency is 29.78%, corresponding to an adsorption capacity of 19.37 mg g⁻¹. Following this, a gradual increase is observed until saturation is attained after 120 minutes, where the adsorption efficiency either slightly declines or levels off. This suggests that most active sites have been occupied, leaving few diazinon molecules in the solution for further adsorption.

Effect of initial diazinon pesticide concentration. Figure 4c illustrates the effect of various diazinon concentrations on the adsorption efficiency, which shows an increase in efficiency up to a concentration of about 40 mg L^− 1, after which the efficiency decreases. At 5 to 20 mg L^− 1 concentrations, the nanozymes have more than enough active sites available for adsorption, and the efficiency increases as more diazinon molecules encounter the adsorbent. At the optimum concentration of 40 mg L^− 1, the maximum adsorption efficiency of 37.82% and the adsorption capacity of 71.56 mg g^− 1 were achieved, indicating that the number of diazinon molecules is balanced with the available active sites on the adsorbent. At a concentration of 50 mg L^− 1, the decrease in adsorption efficiency may be due to the saturation of adsorption sites on the nanozymes, where increasing the diazinon concentration causes unabsorbed molecules to remain in the solution, thus reducing the overall adsorption percentage.

Adsorption Isotherm. Detailed concentration studies were carried out, and isotherm studies were conducted to interpret the retention of diazinon pesticides by rGO/ZnO/CS nanozymes. Figure 5a shows that the adsorption data follow the Langmuir isotherm model, represented by the Ce/qe versus Ce plot. The Langmuir isotherm model assumes that adsorption occurs on a homogeneous adsorbent surface with a limited number of adsorption sites, and each adsorption site can only bind one adsorbate molecule (monolayer adsorption). The R² value = 0.1819 indicates that the experimental data do not fit the Langmuir model well, so the adsorption does not fully follow the assumption of monolayer adsorption on a homogeneous surface. Based on the slope and intercept of this plot, Langmuir constants such as maximum adsorption capacity (q_max) and adsorption equilibrium constant (K_L) can be obtained, showing a low degree of fit, indicating that the adsorption mechanism that occurs may be a more complex than that described by the Langmuir isotherm.

Figure 5b shows the Freundlich isotherm model represented by the plot of log qe versus log Ce. The Freundlich isotherm model describes the adsorption on a heterogeneous surface, where energy varies between adsorption sites. This model also allows multilayer adsorption, which means that diazinon molecules can be adsorbed on top of other molecular layers. The R² value = 0.9138 shows an excellent agreement between the experimental data and the Freundlich model, so it can be concluded that the adsorption of diazinon on rGO/ZnO/CS nanozyme is more by this isotherm model. This regression equation's slope value of 1/n is 1.2051, which provides information about the adsorption intensity. So, the value of n is 0.83 mg g^− 1. Since 1/n > 1, it indicates that the adsorption takes place with a weaker intensity at higher concentrations. The intercept value of log K_f = 0.0619 describes the adsorption capacity. A higher value of K_f indicates a higher adsorption capacity. In this case, the K_f obtained was 1.15 mg g^− 1, indicating that the rGO/ZnO/CS nanozyme material had a moderate adsorption capacity for diazinon. The K_f obtained was 1.15 mg g^− 1, categorized as moderate because this value was not too low (indicating a weak adsorption capacity) but not too high (indicating a powerful adsorption capacity). This indicated that the rGO/ZnO/CS nanozyme could be used as an adsorbent for diazinon on a medium scale. Still, it might be less effective if used to adsorb a more significant amount of adsorbate under different environmental conditions. Overall, the rGO/ZnO/CS nanozyme has the potential to be an efficient adsorbent material for the removal of diazinon pesticides. Still, material modification or different operational conditions could further improve its efficiency.

The study demonstrates the successful development of rGO/ZnO/CS nanozyme for the effective photocatalytic degradation of diazinon pesticide-contaminated water. Characterisation techniques, including FTIR and XRD, confirmed the successful synthesis and structural integrity of the nanozyme, indicating a significant reduction of oxygen-containing functional groups in the rGO and the successful integration of ZnO and chitosan. The morphological analysis through SEM showed a uniform distribution of ZnO nanoparticles on the rGO matrix, enhancing the nanozyme's catalytic activity and biocompatibility. The results indicated that the adsorption efficiency of diazinon improved with increased pH, contact time, and initial concentration of the pesticide. Maximum adsorption efficiency was achieved at a neutral pH, demonstrating optimal conditions for pollutant removal.

Isotherm studies revealed that the adsorption behaviour of diazinon on the rGO/ZnO/CS nanozyme adhered more closely to the Freundlich isotherm model, indicating heterogeneous adsorption characteristics. The calculated adsorption capacity was moderate, suggesting this nanozyme could be used effectively in medium-scale applications for diazinon removal. Overall, the rGO/ZnO/CS nanozyme exhibits promising properties for environmental applications, particularly in the degradation of pesticide contaminants. Future work may optimise operational conditions and modify the nanozyme to enhance its adsorption capacity and efficiency, making it a viable option for water treatment processes.

Conflicts of interest

There are no conflicts to declare.

Author Contribution

F.H.H. is responsible for developing the research concept, project design, and analysing experimental data. M.M. and L.O.A. provided research insights and directed and supervised the project. M.M. offered critical support for the project's pathway towards publication. F.R.R. contributed to nanozyme synthesis, assisted in experimental testing, and processed experimental data. F.H.H., F.R.R., M.M., L.O.A. and M.J. actively discuss research results and are involved in writing, editing and reviewing the final version of the manuscript. All authors approved the final version of this manuscript before submission for publication.

Acknowledgement

The work has received funding from The Kurita Overseas Research Grant 2023 under grant agreement reference number 23Pid093 from the Kurita Water and Environment Foundation (KWEF) 2023.

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Scheme 1 is available in the Supplementary Files section.

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Scheme 1. Synthesis of rGO/ZnO/CS nanozyme and degradation activity in the diazinon pesticide

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Enhanced rGO/ZnO/Chitosan Nanozyme Photocatalytic Technology for Efficient Degradation of Diazinon Pesticide Contaminated Water

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Abstract

Figures

1. Introduction

2. Experimental

2.1 Materials

2.2 Instruments

2.3 Synthesis of Reduced Graphene Oxide (rGO)

2.4 Synthesis of rGO/ZnO/CS Nanozyme

2.5 Measurement of diazinon pesticide standard solution

2.6 Measurement of degradation activity in the diazinon pesticide

3. Results and Discussion

3.1 FTIR Characterization

3.2 XRD analysis

3.3 Structural and morphological characterization

3.4 Maximum Wavelength and Standard Curve

3.5 Degradation Activity of the Diazinon Pesticide

4. Conclusions

Declarations

Conflicts of interest

Author Contribution

Acknowledgement

References

Scheme

Additional Declarations

Supplementary Files

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