Tailored design of three-dimensional rGOA-nZVI catalyst as an activator of persulfate for degradation of organophosphorus pesticides.

In this study, three-dimensional reduced graphene oxide aerogel (rGOA)-supported nanozero-valent iron (rGOA-nZVI) was successfully synthesized via tailored design and applied to activate persulfate (PS) to degrade three organophosphorus pesticides (OPPs; phorate, terbufos and parathion) in water and a historically contaminated soil. The results showed that loading nZVI nanoparticles on rGOA could prevent the aggregation of nZVI. rGOA-nZVI presented a better catalytic performance for PS activation to degrade the three OPPs than bare nZVI and rGOA, with degradation efficiencies of greater than 99.

Efficient degradation of p-nitrophenol by Fe@pomelo peel-derived biochar composites and its mechanism of simultaneous reduction and oxidation process.

In this study, waste pomelo peels (PP) mixed with iron salts was treated successively with hydrothermal and pyrolyzing carbonization processes to obtain Fe(0) containing biochar composites (Fe@PP-Hy-Py) and the catalytic degradation of p-nitrophenol (PNP) using these Fe@PP-Hy-Py composites was studied. The results showed that the hydrothermal pre-treatment of the mixture of iron salts and pomelo peels was favorable for the incorporation of iron precursor within biomass network, which enabled copolymerization during the following pyrolysis. Through the pyrolysis process, the iron precursor was reduced in situ to amorphous Fe(0) dopped into the carbonaceous matrix, which conversely decreased the defect and disorder degree of pseudo-graphitic carbons and catalyzed the formation of environmental persistent free radicals (EPFRs).

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites.

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools.

Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications.

Heavy Metal Pollution and Ecological Assessment around the Jinsha Coal-Fired Power Plant (China).

Heavy metal pollution is a serious problem worldwide. In this study, 41 soil samples and 32 cabbage samples were collected from the area surrounding the Jinsha coal-fired power plant (JCFP Plant) in Guizhou Province, southwest China. Pb, Cd, Hg, As, Cu and Cr concentrations in soil samples and cabbage samples were analysed to study the pollution sources and risks of heavy metals around the power plant.

Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe₃O₄ Composites with the Aid of an Artificial Neural Network and Genetic Algorithm.

Reduced graphene oxide-supported Fe₃O₄ (Fe₃O₄/rGO) composites were applied in this study to remove low-concentration mercury from aqueous solutions with the aid of an artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. The Fe₃O₄/rGO composites were prepared by the solvothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), N₂-sorption, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and superconduction quantum interference device (SQUID). Response surface methodology (RSM) and ANN were employed to model the effects of different operating conditions (temperature, initial pH, initial Hg ion concentration and contact time) on the removal of the low-concentration mercury from aqueous solutions by the Fe₃O₄/rGO composites.

Synthesis and Characterization of Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites Used for Pb(II) Removal.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were prepared by chemical deposition method and were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N₂-sorption and X-ray photoelectron spectroscopy (XPS). Operating parameters for the removal process of Pb(II) ions, such as temperature (20-40 °C), pH (3-5), initial concentration (400-600 mg/L) and contact time (20-60 min), were optimized using a quadratic model. The coefficient of determination (² > 0.

Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables.

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA).

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N₂-sorption, and X-ray photoelectron spectroscopy (XPS) analysis.

Nanoscale zero-valent metals: a review of synthesis, characterization, and applications to environmental remediation.

Engineered nanoscale zero-valent metals (NZVMs) representing the forefront of technologies have been considered as promising materials for environmental remediation and antimicrobial effect, due to their high reducibility and strong adsorption capability. This review is focused on the methodology for synthesis of bare NZVMs, supported NZVMs, modified NZVMs, and bimetallic systems with both traditional and green methods. Recent studies have demonstrated that self-assembly methods can play an important role for obtaining ordered, controllable, and tunable NZVMs.