Initial Pesticide Concentration Research Articles

Kinetics and sorption behavior of glyphosate and tricyclazole for their efficient retention in biomixtures

The present investigation aims to study adsorption-desorption behavior of glyphosate and tricyclazole in rice straw-compost biomixtures. To enhance pesticide adsorption and performance of the bio-purification system, rice straw-compost (BM) biomixture was mixed with wheat straw biochar (WBC, 1% and 5%), and adsorption of both pesticides in control (BM) and WBCBM(1%) and WBCBM(5%) biomixtures was compared. The kinetics study suggested that the pseudo-second-order model best explained the time-dependent adsorption of both pesticides and intraparticle adsorption was not the rate-determining step. Tricyclazole was more sorbed than glyphosate in all biomixtures which can be attributed to its lower water solubility. The WBC increased the sorption of both pesticides, but the effect varied with the nature of pesticides and biochar content. The adsorption coefficient values in BM, WBCBM(1%), and WBCBM(5%) biomixtures were 26.74, 38.16, and 51.97 (glyphosate) and 38.07, 59.94, and 84.54 (tricyclazole), respectively. The adsorption data was subjected to the Freundlich, the Langmuir, and the Temkin isotherms, and among them, the Freundlich isotherm best explained pesticide adsorption behavior. Desorption results suggested that the adsorption of glyphosate was more irreversible than tricyclazole and depended upon initial pesticide concentration. This study suggested that biochar mixed rice straw-compost biomixtures can be exploited in bio-purification systems for glyphosate and tricyclazole.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.

Anaerobic Treatment of N-(Phosphonometyl) Glycine Using Mixed Culture in Batch Reactor

Pesticides have been used in the fight against various pests to increase crop growth in our country and the world. In recent years, high concentrations of toxic and detection of carcinogenic pesticides indicates that these substances are transported to groundwater and pollute water resources. N- (phosphonomethyl) glycine are also used in large quantities. In this study, the optimization of anaerobic treatment of N- (phosphonomethyl) glycine was investigated by applying a statistical-based experimental design. Full factorial trials different initial pesticide concentrations and cosubstrate type were established, making up 9 different experimental setups. Trials were performed in 2 replicates. The experiments were carried out in Oxitop C bottles with stirring at a working volume of 200 mL. The pH was adjusted to 7 ± 0.2. Trials were performed at 35oC for 30 days. At the end of the process, the removal of the trials according to the input and output COD and pesticide values were examined. As a result, the most efficient COD removal was achieved 98,96 % when pesticide concentration of 5 mg L-1 and glucose as cosubstrate were used. The highest pesticide removal was found to be 74,83% when pesticide concentration of 25 mg L-1 and glucose was used as cosubstrate.

Study of Factors Influencing the Oral Bioaccessibility of Commonly Used and Detected Pesticides in Bananas and Mangoes Based on in vitro Methods.

Estimating the impact of pesticide residue bioaccessibility in fruits on dietary exposure is a complex task in human health risk assessment. This research investigated the bioaccessibility of ten commonly used and detected pesticides in bananas and mangoes, as well as the factors influencing it, using an in vitro model. The highest bioaccessibility was observed at pH levels of 2.5 and 6.5 in the gastric and intestinal stages, respectively. Bioaccessibility decreased significantly with increasing solid/liquid ratios for most pesticides. The consumption of protein and four dietary components (carbohydrates, protein, lipids, and dietary fiber) could significantly reduce pesticide bioaccessibility by 9.89-48.32% (p < 0.05). Bioaccessibility in oral and gastric stages among four populations followed the order of adults/the elderly > children > infants, due to decreasing concentrations of α-amylase and pepsin. Pesticides in bananas generally exhibited a higher bioaccessibility (18.65-82.97%) compared to that in mangoes (11.68-87.57%). Bioaccessibility showed a negative correlation with the Log P values of the target pesticide, while no clear relationship was found between bioaccessibility and initial pesticide concentrations. Incorporating bioaccessible pesticide concentrations into risk assessments could lower dietary risk estimates by 11.85-79.57%. Assessing human exposure to pesticides based on bioaccessibility would greatly improve the accuracy of the risk assessment.

Photocatalytic degradation of four organophosphorus pesticides in aqueous solution using D-cys/Au NPs modified TiO2 by natural sunlight

The widespread use of organophosphorus pesticides (OPPs) in foods, poses a serious threat to human and environmental health, it is crucial to develop methods that can efficiently degrade residual OPPs. Herein, a chiral TiO2 photocatalyst was developed by modification TiO2 with D-cys/Au NPs (DAT) for the simultaneous degradation of four OPPs (chlorpyrifos, chlorpyrifos-methyl, malathion, and parathion) by natural light. The influence of D-Au NPs concentration on the degradation process was optimized, along with the initial pesticide concentration. Under optimal conditions, the photocatalytic degradation efficiencies of DAT for four OPPs were 96.7 %, 56.9 %, 42.6 %, and 49.2 %, respectively, after 12 h of natural light exposure. 12 degradation products of OPPs were identified, among which three products were identified for the first time. The degradation pathway revealed that the degradation of OPPs by DAT involves the oxidation of the P═S bond and the cleavage of the P-O bond and C-O bond. This study establishes a theoretical foundation for applying chiral TiO2 nanomaterials for the photocatalytic degradation of OPPs in foods.

An investigation of Ca-doped MgO nanoparticles for the improved catalytic degradation of thiamethoxam pesticide subjected to visible light irradiation

The remediation of pesticides from the environment is one of the most important technology nowadays. Herein, magnesium oxide (MgO) nanoparticles and calcium-doped magnesium oxide (Ca-doped MgO) nanoparticles were synthesized by the co-precipitation method and were used for the degradation of thiamethoxam pesticide in aqueous media. Characterization of the MgO and Ca-doped MgO nanoparticles were performed by XRD, SEM, EDX, and FT-IR analysis to verify the synthesis and variations in chemical composition. The band gap energy and crystalline size of MgO and Ca-doped MgO nanoparticles were found to be 4.8 and 4.7 eV and 33 and 34 nm respectively. The degradation of thiamethoxam was accomplished regarding the impact of catalyst dosage, contact time, temperature, pH, and initial pesticide concentration. The pH study indicates that degradation of thiamethoxam depends on pH and maximum degradation (66%) was obtained at pH 5 using MgO nanoparticles. In contrast, maximum degradation (80%) of thiamethoxam was observed at pH 8 employing Ca-doped MgO nanoparticles. The percentage degradation of thiamethoxam was initially increasing but decreased at higher doses of the catalysts. The degradation of the pesticide was observed to be increased with an increase in contact time while high at room temperature but decreased with a temperature rise. The effect of the initial concertation of pesticide indicates that degradation of pesticide increases at low concentrations but declines at higher concentrations. This research study reveals that doping of MgO nanoparticles with calcium enhanced the degradation of thiamethoxam pesticide in aqueous media.

A Novel Nanoporous Adsorbent for Pesticides Obtained from Biogenic Calcium Carbonate Derived from Waste Crab Shells

A novel nanoporous adsorbent was obtained through the thermal treatment and chemical wash of the wasted crab shells (BC1) and characterized by various techniques. The structure of BC1 at the end of the treatments comprised a mixture of calcite and amorphous CaCO3, as evidenced by X-ray diffraction and Fourier transform infrared absorption. The BET surface area, BET pore volume, and pore diameter were 250.33 m2 g−1, 0.4 cm3 g−1, and &lt;70 nm, respectively. The point of zero charge of BC1 was determined to be around pH 9. The prepared adsorbent was tested for its adsorption efficacy towards the neonicotinoid pesticide acetamiprid. The influence of pH (2–10), temperature (20–45 °C), adsorbent dose (0.2–1.2 g L−1), contact time (5–60 min), and initial pesticide concentration (10–60 mg L−1) on the adsorption process of acetamiprid on BC1 was studied. The adsorption capacity of BC1 was 17.8 mg g−1 under optimum conditions (i.e., 20 mg L−1 initial acetamiprid concentration, pH 8, 1 g L−1 adsorbent dose, 25 °C, and 15 min contact time). The equilibrium data obtained from the adsorption experiment fitted well with the Langmuir isotherm model. We developed an effective nanoporous adsorbent for the recycling of crab shells which can be applied on site with minimal laboratory infrastructure according to local needs.

Enhancement of photocatalytic activity of Ba-doped CoO for degradation of Emamectin benzoate in aqueous solution.

The present study was focused on the preparation of cobalt oxide (CoO) and barium-doped cobalt oxide (Ba-doped CoO) by following the co-precipitation method for the degradation of Emamectin benzoate pesticide in the aqueous medium. The prepared catalysts were characterized using SEM, EDX, and XRD to confirm the formation of catalysts and to observe the variation in the composition of catalysts during the degradation study. It can be suggested from the results of SEM, EDX, XRD, and FTIR analyses that Ba atom has successfully incorporated in the crystalline structure of CoO. The degradation of Emamectin benzoate pesticide was studied under the influence of different factors like solution pH, the dose of catalyst, contact time, temperature, and initial concentration of pesticide. It was observed that solution pH affects the degradation of the pesticide, and maximum degradation (23% and 54%) was found at pH 5.0 and 6.0 using CoO and Ba-doped CoO, respectively. The degradation of pesticides was found to be increased continuously (27-35% in case of CoO while 47-58% in case Ba-doped CoO) with the time of contact. However, the degradation was found to be decreased (23-3% in case of CoO while 47-44% in case Ba-doped CoO) with an increase in temperature. Likewise, in the beginning, degradation was observed to be increased up to some extent with the dose of catalyst and initial concentration of pesticide but started to decrease with further augmentation in the dose of catalyst and initial concentration of pesticide. It may be concluded from this study that doping of Ba considerably enhanced the photocatalytic ability of CoO for Emamectin benzoate pesticide.

Photocatalytic degradation and removal of type II pyrethroid pesticide (lambda-cyhalothrin) residue from wastewater using nanoceria for agricultural runoff application

Synthetic chemical pesticides' nature, mode of action, and persistence have brought about debates regarding whether the end justifies the means. Lambda-cyhalothrin is an important insecticide for farmers and households, with great accessibility and excellent action against pests and disease-carrying insects. Like other pyrethroid pesticides, Lambda-cyhalothrin targets the nervous system of insects or pests. However, its fate in the environment, especially in water and living systems, has made it crucial to explore methods of treating or degrading its residues in the environment. The present study aimed to develop a suitable method for the photocatalytic degradation and removal of Lambda-cyhalothrin (LCY) from wastewater and agricultural runoff. The nanoceria were used under natural solar irradiation without applying any scavenger chemical or buffer. This was synthesized using a simple co-precipitation method using cerium nitrate hexahydrate as a precursor. The synthesized nanoceria were characterized using X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), Fourier Transform Infrared spectroscopy (FTIR) and particle size analysis. The average size of the particle was 27 nm. The photocatalytic degradation was conducted in batches with various pesticide concentrations exposed to different amounts of nanoceria. The initial and final concentrations of the LCY at each level were determined using Shimadzu UV spectroscopy. At optimum conditions, nanoceria was found to degrade and remove more than 63% of the initial pesticide concentration. This method can be suitable for degrading and removing pesticide residue from agricultural runoff (at source) and industrial effluents from synthetic pesticide industries.

Insights into the effect of parameters and pathway of visible-light photodegradation of glyphosate and diazinon by C-TiO2/clinoptilolite nanocomposite

In this study, Carbon-doped TiO2/clinoptilolite nanocomposite was synthesized by sol–gel method, and it was investigated for the destruction of two organophosphorus pesticides, glyphosate, and diazinon, in an aqueous environment under visible light. The synthesized catalyst was characterized using FE-SEM, TEM, FT-IR, XRD, EDX, BET, and DRS techniques. The degradation efficiencies of 84% and 89.67% were obtained for glyphosate and diazinon under optimal conditions (initial concentration of pesticides: 30 mg L−1, solution pH of glyphosate: 3, solution pH of diazinon: 6, catalyst dosage: 0.3 g L−1), respectively. The photocatalytic degradation of both pesticides followed the pseudo-first-order kinetics. Also, photodegradation mechanisms and pathways were investigated. In this research, a new catalyst with high efficiency was synthesized, which can be used for the degradation of other organic compounds.

Facile adsorption of organophosphate pesticides over HKUST-1 MOFs.

The recovery of organophosphate pesticides (OPPs) from aqueous solutions is imperative considering their agricultural and environmental implications. Among various mitigation approaches used for OPPs' removal, adsorption offers many advantageous features for OPPs abatement owing to its benign nature, cost-effective processing, and non-requirement of excessive equipment. This research describes the adsorptive removal of three organophosphate pesticides (OPPs) namely chlorpyrifos (CPF), methyl parathion (MP), and malathion (MAL) by HKUST-1 (HKUST = Hong Kong University of Science and Technology) metal-organic framework (MOF). The synthesis of HKUST-1 MOFs was confirmed by various spectroscopic and microscopic techniques. The adsorption kinetics was systematically investigated by varying three parameters to include solution pH, contact time, and initial pesticide concentration. Among all the three pesticides, HKUST-1 showed enhanced removal of CPF in terms of pH, resulting in an adsorption capacity of 1.82mg·g-1. However, under the effect of contact time at 60min, the adsorption capacity of HKUST-1 for PM, MAL, and CPF were computed to be 1.83, 1.79, and 0.44mg·g-1, respectively. Besides, HKUST-1 showed a remarkable performance towards adsorptive removal of MAL (14.01mg·g-1 at 10mg·L-1 concentration) with linear increase in adsorption capacity as the function of initial pesticide concentration. The MOFs were also able to retain ca. 50% of their adsorption efficiency over the course of five cycles of adsorptive removal of CP. In the future, a comprehensive data table showing the performance of various MOFs against various OPPs can be constructed on the basis of parameters used in this study.

Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms.

Machine learning (ML) models have become a potent tool for advancing environmentally conscious research in materials science, allowing the prediction of wastewater treatment efficacy using eco-materials. In this study, we showcase the potential of an advanced decision tree-based ensemble learning algorithm to model the eviction of emerging organophosphate-based pesticidal pollutants in aqueous systems. The model is trained using laboratory-based biochar adsorption data, and it establishes the relationship between independent experimental factors and the % organophosphate pesticide adsorption efficiency as the output parameter. We classified the experimental dataset into input and output parameters to build the model. The input parameters included pyrolysis temperature, solution pH, surface area, pore volume, and initial pesticide concentration. Grid search optimization in Python was employed to train the model using sets of input-output patterns. The results indicated that the XGBoost-based ensemble ML framework provides the best forecast for pesticide adsorption on the biochar matrix, achieving high scores for the regularization coefficient (R2train = 0.998, R2test = 0.981). The concentration of the organophosphorus compound and the morphology of biochar significantly influenced the pesticide adsorption behavior. These findings demonstrate the potential of using ensemble learning algorithms for the balanced design of carbon-enriched biomaterials to remove emerging micropollutants from water effectively.

UiO-66 (Zr-MOF): Synthesis, Characterization, and Application for the Removal of Malathion and 2, 4-D from Aqueous Solution

ABSTRACT UiO-66 (Zr-MOF) metal-organic framework was successfully synthesized by the solvothermal method and characterized using FTIR, PXRD and TGA. Its ability to remove Malathion, 2,4-Dichlorophenoxyacetic acid (2,4-D) and a mixture of the two from an aqueous solution was studied. Effects of different experimental parameters (pH, adsorbent dose, contact time, speed of agitation and initial pesticide concentrations) were studied. Adsorption isotherm study revealed that the equilibrium data well fitted the Freundlich, Langmuir and Jovanovic isotherm models. Similarly, the kinetics study showed that the pseudo-first-order model gave the best fitting. Thermodynamic study indicated that the adsorption process was spontaneous and exothermic. To evaluate the matrix effect, tap water samples were spiked with standard solutions of the analytes and recoveries in the range of 70–120% were obtained. Based on the findings of the present study, UiO-66 (Zr-MOF) could be taken as a promising adsorbent for the removal of pesticides from aqueous samples

Encapsulation of Copper Nanoparticles in Electrospun Nanofibers for Sustainable Removal of Pesticides

The excellent catalytic properties of copper nanoparticles (CuNPs) for the degradation of the highly toxic and recalcitrant chlorpyrifos pesticide are widely known. However, CuNPs generally present low stability caused by their high sensitivity to oxidation, which leads to a change of the catalytic response over time. In the current work, the immobilization of CuNPs into a polycaprolactone (PCL) matrix via electrospinning was demonstrated to be a very effective method to retard air and solvent oxidation and to ensure constant catalytic activity in the long term. CuNPs were successfully anchored into PCL electrospun fibers in the form of Cu2O at different concentrations (from 1.25 wt % to 5 wt % with respect to the PCL), with no signs of loss by leaching out. The PCL mats loaded with 2.5 wt % Cu (PCL-2.5Cu) almost halved the initial concentration of pesticide (40 mg/L) after 96 h. This process was performed in two unprompted and continuous steps that consisted of adsorption, followed by degradation. Interestingly, the degradation process was independent of the light conditions (i.e., not photocatalytic), expanding the application environments (e.g., groundwaters). Moreover, the PCL-2.5Cu composite presents high reusability, retaining the high elimination capability for at least five cycles and eliminating a total of 100 mg/L of chlorpyrifos, without exhibiting any sign of morphological damages.

Modeling and optimization of chlorpyrifos and glyphosate biodegradation using RSM and ANN: Elucidating their degradation pathways by GC-MS based metabolomics

Ongoing and extensive use of pesticides negatively impact the environment and human health. Microbe-based remediation bears importance as it is an eco-friendly and cost-effective technique. The present study investigated chlorpyrifos (CHL) and glyphosate (GLY) degrading potential of Bacillus cereus AKAD 3–1, isolated from the soybean rhizosphere. Optimization and validation of different process variables were carried out by response surface methodology (RSM) and artificial neural network (ANN). Critical parameters which affect the degradation process are initial pesticide concentration, pH, and inoculum size. At optimum conditions, the bacterial strain demonstrated 94.52% and 83.58% removal of chlorpyrifos and glyphosate, respectively. Both Central-composite design (CCD-RSM) and ANN approaches proved to perform well in modeling and optimizing the growth conditions. The optimum ANN-GA model resulted in R2 ≥ 0.99 for chlorpyrifos and glyphosate, while in the case of RSM, the obtained R2 value was 0.96 and 0.95, respectively. Results indicated that the process variables significantly (p < 0.05) impact chlorpyrifos and glyphosate biodegradation. Moreover, the predicted RSM model had a “lack of fit p-value” of “0.8849” and “0.2502” for chlorpyrifos and glyphosate, respectively. GC-MS analysis revealed that the strain first converted chlorpyrifos into 3,5,6-trichloro pyridin-2-ol & O, O-diethyl O-hydrogen phosphorothiate. Later, these intermediate metabolites were broken and completely mineralized into non-toxic by-products. Similarly, glyphosate was first converted into 2-(methylamino) acetic acid and amino-oxyphosphonic acid, which were further mineralized without any toxic by-products. Taken together, the results of this study clarify the biodegradation pathways and highlights the promising potential of B. cereus AKAD 3–1 in the bioremediation of chlorpyrifos and glyphosate-polluted environments.

Study on Rapid Detection of Pesticide Residues in Shanghaiqing Based on Analyzing Near-Infrared Microscopic Images.

Aiming at guiding agricultural producers to harvest crops at an appropriate time and ensuring the pesticide residue does not exceed the maximum limit, the present work proposed a method of detecting pesticide residue rapidly by analyzing near-infrared microscopic images of the leaves of Shanghaiqing (Brassica rapa), a type of Chinese cabbage with computer vision technology. After image pre-processing and feature extraction, the pattern recognition methods of K nearest neighbors (KNN), naïve Bayes, support vector machine (SVM), and back propagation artificial neural network (BP-ANN) were applied to assess whether Shanghaiqing is sprayed with pesticides. The SVM method with linear or RBF kernel provides the highest recognition accuracy of 96.96% for the samples sprayed with trichlorfon at a concentration of 1 g/L. The SVM method with RBF kernel has the highest recognition accuracy of 79.16~84.37% for the samples sprayed with cypermethrin at a concentration of 0.1 g/L. The investigation on the SVM classification models built on the samples sprayed with cypermethrin at different concentrations shows that the accuracy of the models increases with the pesticide concentrations. In addition, the relationship between the concentration of the cypermethrin sprayed and the image features was established by multiple regression to estimate the initial pesticide concentration on the Shanghaiqing leaves. A pesticide degradation equation was established on the basis of the first-order kinetic equation. The time for pesticides concentration to decrease to an acceptable level can be calculated on the basis of the degradation equation and the initial pesticide concentration. The present work provides a feasible way to rapidly detect pesticide residue on Shanghaiqing by means of NIR microscopic image technique. The methodology laid out in this research can be used as a reference for the pesticide detection of other types of vegetables.

Ultrasound-assisted degradation of organophosphorus pesticide methidathion using CuFe2O4@SiO2-GOCOOH as a magnetic separable sonocatalyst.

Water contamination by pesticides is a critical environmental issue, necessitating the development of sustainable and efficient degradation methods. This study focuses on synthesizing and evaluating a novel heterogeneous sonocatalyst for degrading pesticide methidathion. The catalyst consists of graphene oxide (GO) decorated CuFe2O4@SiO2 nanocomposites. Comprehensive characterization using various techniques confirmed the superior sonocatalytic activity of the CuFe2O4@SiO2-GOCOOH nanocomposite compared to CuFe2O4@SiO2 alone. The enhanced performance is attributed to the combined effects of GO and CuFe2O4@SiO2, including increased surface area, enhanced adsorption capabilities, and efficient electron transfer pathways. Reaction parameters such as time, temperature, concentration, and pH significantly influenced the degradation efficiency of methidathion. Longer reaction times, higher temperatures, and lower initial pesticide concentrations favored faster degradation and higher efficiency. Optimal pH conditions were identified to ensure effective degradation. Remarkably, the catalyst demonstrated excellent recyclability, indicating its potential for practical implementation in pesticide-contaminated wastewater treatment. This research contributes to the development of sustainable methods for environmental remediation, highlighting the promising potential of the graphene oxide decorated CuFe2O4@SiO2 nanocomposite as an effective heterogeneous sonocatalyst for pesticide degradation.

Green biosynthesized N-doped Bi2O3@SnO2 nanocomposite for efficient remediation of endocrine disrupting pesticides

Many persistent and low-cost pesticides are regularly used for domestic, agricultural, and industrial purpose. Bioaccumulation and toxicity of pesticides have raised environmental concerns, highlighting the need for effective removal techniques by using nanomaterials. The ability of hybrid nitrogen incorporated bismuth oxide with stannous oxide (N-Bi2O3@SnO2) were utilized for the removal of Ethion (ET) and Toxaphene (TP) pesticides. Leaves of A. indica (commonly found plant in India) employed as a natural surfactant and deionised water as a greener solvent. The purity and crystallinity of the deformed spherical nanoparticle (10–80 nm) were ensured by the sharp peaks acquired by PXRD. Subsequently, N-Bi2O3@SnO2 nanocomposite was investigated for the elimination of pesticides at discrete reaction parameters (pollutant: 2–10 mgL−1; catalyst: 15–35 mg; pH: 3–11, dark-sunlight). N-Bi2O3@SnO2 nanocomposite showed maximum degradation (ET:96 % and TP:90 %) with 30 mg catalytic dosage, at initial concentration of pesticide (2 mgL−1) at neutral pH under sunlight irradiation. Larger surface area (75 m2g−1), more negative value of zeta potential (–23.4 mV), and lower bandgap (2.1 eV) supported the effective photocatalytic efficiency of N-Bi2O3@SnO2 nanocomposite. The presence of holes, O2, and OH radicals were responsible for the breakdown of the complex structure of ET and TP, confirmed by scavenger analysis. Safer metabolites formed after degradation confirmed by GC–MS analysis. Conclusively, by virtues greater efficiency of reusability (n = 8), stability, and greater charge separation, hybrid N-Bi2O3@SnO2 nanocomposite may prove as an alternative catalyst for industrial application with intense scope.

Photocatalytic activity of graphene oxide-TiO2 nanocomposite on dichlorvos and malathion and assessment of toxicity changes due to photodegradation

Heterogeneous photocatalysis was used for the removal of two widely used organophosphorus pesticides, dichlorvos, and malathion from water. Graphene oxide-TiO2 nanocomposite (GOT) was synthesized and used as a photocatalyst for the removal of these pesticides. Batch studies for optimizing photocatalytic degradation and mineralization of pesticides over 80 min were conducted by varying the pH (2–10), catalyst dose (20 mg/L-200 mg/L), and initial pesticide concentration (0.5 mg/L-20 mg/L), and the irradiation source (125 W UV and visible lamp). Degradation kinetics for the pesticides were evaluated. Ellman assay was used to estimate the toxic effect of pesticides and evaluate toxicity reduction due to treatment. The highest degradation and mineralization of dichlorvos and malathion was observed at pH 6 and the optimum catalyst dose was 60 mg/L. Under UV irradiation, 80% and 90% degradation were observed for dichlorvos and malathion, respectively for 0.5 mg/L initial pesticide concentration. The photocatalytic degradation reaction followed Langmuir-Hinshelwood kinetics. A high degree of mineralization was achieved for both the pesticides. Analysis of the results revealed that the residual toxic effect after photocatalysis was primarily due to the residual parent compound. A comparative study revealed that GOT yielded better pesticide degradation compared to commercially available TiO2 under both UV and visible irradiation.

Adsorption properties of graphene materials for pesticides: Structure effect

In recent years, the application of graphene materials to the adsorption of pesticides has attracted great research interests. However, to the best of our knowledge, there has been no report about the specific adsorption mechanism of graphene materials for pesticides with different structures. This study investigated the potential adsorption mechanism of graphene oxide (GO) and reduced graphene oxide (rGO) for three pesticides with different structures [Methomyl (Met; without aromatic rings), Acetamiprid (Ace; one aromatic ring) and Azoxystrobin (Azo, three aromatic rings)]. The operating variables including time, temperature and initial pesticide concentration were estimated and optimized with face-centered composite design (FCCD) through response surface methodology (RSM). Under optimal conditions, the calculated adsorption capacity of GO for Met, Ace and Azo was 106.22, 285.96 and 2896.84 mg/g; while that of rGO was 96.86, 357.65 and 2818.04 mg/g, respectively. The adsorption kinetics follows the pseudo second-order and Elovich models, while the adsorption isotherm can be well described by the Sips model. Thermodynamic study revealed that the adsorption process of the three pesticides onto GO or rGO was an exothermic and physical process. The adsorption was deduced to be synergistically driven by (1) the π-π conjugation between the sp2 region of adsorbent and the aromatic ring of pesticide molecules, (2) H-bonding and electrostatic interaction between heteroatoms in pesticide molecules and oxygen-containing functional groups on adsorption materials, and (3) H-bonding and π-π conjugation between pesticides. In general, this study provides important practical and theoretical implications for understanding the molecular mechanism underlying the adsorption of pollutants by GO or rGO and its practical application.