Visible light assisted mineralization of malachite green dye by green synthesized xanthan gum/agar@ZnO bionanocomposite

Eco-friendly sunlight driven photocatalytic remediation of water pollutants using N-doped NiO integrated into a guar gum-agar polymeric network

Hydrogel based photocatalysts with strong photocatalytic and adsorption capacities have promising future in wastewater treatment. Herein, a novel nitrogen doped NiO/guargum agar-agar nanocomposite hydrogel (GGAA@N-NiO) was synthesized via facile co-precipitation and in-situ methods. Characterization using relevant tools confirmed successful inclusion of N-NiO in the hydrogel-matrix. The higher zeta-potential of GGAA@N-NiO (-23.4) than N-NiO (-16.3) confirms the greater stability. The average crystallite size for NiO (7.82 nm) and N-NiO (4.38 nm) was calculated using the Scherrer equation and, NiO (5.7 nm) and N-NiO (2.31 nm) using WH plot. FESEM image, showed particle size of N-NiO 79.4 nm. Band gaps 2.70 eV, 2.75 eV, and 3.03 eV for GGAA@N-NiO, N-NiO, and NiO, respectively, were calculated using Kubelka Munk plots. The lower PL intensity of GGAA@N-NiO indicates a suppressed electron-hole (e-/h+) recombination rate, which reflects its enhanced photocatalytic activity. GGAA@N-NiO achieved 95.3 % removal of benzyl butyl phthalate (BBP) and 94.6 % removal of para-cresol (PC) within 240 min under optimized conditions (i.e. pH ∼7.0; catalyst-dose: 1.5 g/L with BBP and 1.0 g/L with PC, pollutant-concentration: 20 ppm for BBP and, 50 ppm for PC). The removal followed first-order-kinetics and Langmuir-isotherm, with ·OH radicals exhibiting a dominant role in the photocatalytic-removal process. GC-MS analysis confirmed the degradation of BBP and PC into safer metabolites. The nanocomposite was reusable over six cycles, demonstrating stability. This study offers cost-effective and highly-efficient approach to developing stable hydrogel-supported photocatalysts for water-remediation.

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.

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William's plot, demonstrating a minimal number of outliers (66-71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

Review on emerging trends and challenges in the modification of xanthan gum for various applications

This review explores the realm of structural modifications and broad spectrum of their potential applications, with a special focus on the synthesis of xanthan gum derivatives through graft copolymerization methods. It delves into the creation of these derivatives by attaching functional groups (-OH and -COOH) to xanthan gum, utilizing a variety of initiators for grafting, and examining their diverse applications, especially in the areas of food packaging, pharmaceuticals, wastewater treatment, and antimicrobial activities. Xanthan gum is a biocompatible, biodegradable, less toxic, bioactive, and cost-effective natural polymer derived from Xanthomonas species. The native properties of xanthan gum can be improved by cross-linking, grafting, curing, blending, and various modification techniques. Grafted xanthan gum has excellent biodegradability, metal binding, dye adsorption, immunological properties, and wound healing ability. Owing to its remarkable properties, such as biocompatibility and its ability to form gels resembling the extracellular matrix of tissues, modified xanthan gum finds extensive utility across biomedicine, engineering, and the food industry. Furthermore, the review also covers various modified derivatives of xanthan gum that exhibit excellent biodegradability, metal binding, dye adsorption, immunological properties, and wound healing abilities. These applications could serve as important resources for a wide range of industries in future product development.

Selective adsorption and separation of methylene blue by facily preparable xanthan gum/amantadine composites

In this work, xanthan gum-based composites were successfully graft-modified by amantadine (XG-Fe³⁺/AM) with higher adsorption capacity and selectivity on recycling cationic dye (methylene blue, MB) from aqueous solution. The adsorption equilibrium of MB could be achieved approximately within 5 min when the initial concentration was 100 mg/L, and the maximum adsorption capacity was up to 565 mg/g. After 5 desorption-regeneration cycles, the removal rate of XG-Fe³⁺/AM for MB could still be as high as 95 % with slight decrement. Additionally, the effects of pH, contact time, temperature and initial dye concentration on the adsorption performance of MB were systematically examined. Furthermore, the adsorbent was characterized by FT-IR, BET and XPS analysis. In mixed anionic and cationic dyes, the adsorption selectivity of XG-Fe³⁺/AM on MB in the mixture of MB and methyl orange (MO) reached up to 99.69 %. Molecular dynamics simulation revealed that the trend of adsorption energy for dyes was in good agreement of the experimental order of adsorption capacities and molecular sizes among seven anionic and cationic dyes based on molecular matching effect and electrostatic interaction. Therefore, XG-Fe³⁺/AM is an eco-friendly, facile-synthesis and high-selectivity adsorbent, which remove cationic dyes in multi-component systems through electrostatic interaction and molecular matching effect.

CuO Nanorods Immobilized Agar-Alginate Biopolymer: A Green Functional Material for Photocatalytic Degradation of Amaranth Dye

The contamination of water is increasing day by day due to the increase of urbanization and population. Textile industries contribute to this by discarding their waste directly into water streams without proper treatment. A recent study explores the treatment potential of copper oxide nanorods (CuO NRs) synthesized on a green basis in the presence of a biopolymer matrix of agar (AA) and alginate (Alg), in terms of cost effectiveness and environmental impact. The synthesized bio nanocomposite (BNC) was characterized by using different instrumental techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), ultra-violet spectroscopy (UV-Vis), scanning electron microscopy-energy dispersive X-ray-elemental analysis (SEM-EDX), transmission electron microscopy (TEM), selected area diffraction pattern (SAED) and X-ray photoelectron spectroscopy (XPS). The optical studies revealed that immobilization of CuO NRs with Alg-Agar biopolymer blend resulted in an increase in light absorption capacity by decreasing the energy bandgap from 2.53 eV to 2.37 eV. The bio nanocomposite was utilized as a photocatalyst for the degradation of amaranth (AN) dye from an aquatic environment under visible light irradiation. A statistical tool known as central composite design (CCD) associated with response surface methodology (RSM) was taken into consideration to evaluate the optimized values of process variables and their synergistic effect on photocatalytic efficiency. The optimized values of process variables were found to be irradiation time (45 min), AN concentration (80 ppm), catalyst dose (20 mg), and pH (4), resulting in 95.69% of dye degradation at 95% confidence level with desirability level 1. The rate of AN degradation was best defined by pseudo-first-order reaction based on the correlation coefficient value (R2 = 0.99) suggesting the establishment of adsorption-desorption equilibrium initially at the catalyst surface then photogenerated ^•O₂^- radicals interacting with AN molecule to mineralize them into small non-toxic entities like CO₂, H₂O. The material used has high efficiency and stability in photocatalytic degradation experiments up to four cycles of reusability.

Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green

This study focuses on the potential capability of numerous machine learning models, namely CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting machine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO₃ composites. A comprehensive databank of 1200 data points was generated under various experimental conditions. The ten input variables selected were the catalyst type, reaction time, light intensity, initial concentration, catalyst loading, solution pH, humic acid concentration, anions, surface area, and pore volume of various photocatalysts. The MG dye degradation efficiency was selected as the output variable. An evaluation of the performance metrics suggested that the CatBoost model, with the highest test coefficient of determination (0.99) and lowest mean absolute error (0.64) and root-mean-square error (1.34), outperformed all other models. The CatBoost model showed that the photocatalytic reaction conditions were more important than the material properties. The modeling results suggested that the optimized process conditions were a light intensity of 105 W, catalyst loading of 1.5 g/L, initial MG dye concentration of 5 mg/L and solution pH of 7. Finally, the implications and drawbacks of the current study were stated in detail.

Enhanced photocatalytic degradation of malachite green dye by highly stable visible-light-responsive Fe-based tri-composite photocatalysts

A novel visible-light-sensitive ZnVFeO₄ photocatalyst has been fabricated by the precipitation method at different pH values for the enhanced photocatalytic degradation of malachite green (MG) dye as a representative pollutant under visible light irradiation at neutral pH conditions. The structure and optical characteristics of the prepared photocatalysts were investigated by XRD, FTIR, N2 adsorption-desorption, TEM, diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) analyses. In addition, the photocatalytic activity of ZnVFeO₄ photocatalysts superior the efficiency to be more than that of the mono and bi-metal oxides of iron and iron zinc oxides, respectively. The best sample, ZnVFeO₄ at pH 3, significantly enhances the degradation rate under visible light to be 12.7 × 10^-3 min^-1 and can retain a stable photodegradation efficiency of 90.1% after five cycles. The effect of the catalyst dose and the initial dye concentration on the photodegradation process were studied. This promising behavior under visible light may be attributed to the low bandgap and the decreased electron-hole recombination rate of the ZnVFeO₄ heterostructures. The scavenger experiment confirmed that the hydroxyl radicals induced the MG photodegradation process effectively. Hence, the ZnVFeO₄ is a reliable visible-light-responsive heterostructure photocatalyst with excellent potential for the photodegradation of organic pollutants in wastewater treatment.