Mingyi Fan

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. In addition to common characterization methods, the state-of-the-art methods have been developed to obtain the properties of NZVMs (e.g., granularity, size distribution, specific surface area, shape, crystal form, and chemical bond) with the resolution down to subnanometer scale. These methods include spherical aberration corrected scanning transmission electron microscopy (Cs-corrected STEM), electron energy-loss spectroscopy (EELS), and near edge X-ray absorption fine structure (NEXAFS). A growing body of experimental data has proven that nanoscale zero-valent iron (NZVI) is highly effective and versatile. This article discusses the applications of NZVMs to treatment of heavy metals, halogenated organic compounds, polycyclic aromatic hydrocarbons, nutrients, radioelements, and microorganisms, using both ex situ and in situ methods. Furthermore, this paper briefly describes the ecotoxicological effects for NZVMs and the research prospects related to their synthesis, modification, characterization, and applications.

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 (R2 > 0.99) obtained for the mathematical model indicates a high correlation between the experimental and predicted values. The optimal temperature, pH, initial concentration and contact time for Pb(II) ions removal in the present experiment were 21.30 °C, 5.00, 400.00 mg/L and 60.00 min, respectively. In addition, the Pb(II) removal by nZVI/rGO composites was quantitatively evaluated by using adsorption isotherms, such as Langmuir and Freundlich isotherm models, of which Langmuir isotherm gave a better correlation, and the calculated maximum adsorption capacity was 910 mg/g. The removal process of Pb(II) ions could be completed within 50 min, which was well described by the pseudo-second order kinetic model. Therefore, the nZVI/rGO composites are suitable as efficient materials for the advanced treatment of Pb(II)-containing wastewater.

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. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R2 value than the pseudo-first-order model.

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. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.

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. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

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. The ANN-GA model results (with a prediction error below 5%) show better agreement with the experimental data than the RSM model results (with a prediction error below 10%). The removal process of the low-concentration mercury obeyed the Freudlich isotherm and the pseudo-second-order kinetic model. In addition, a regeneration experiment of the Fe3O4/rGO composites demonstrated that these composites can be reused for the removal of low-concentration mercury from aqueous solutions.

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. The results indicate that the JCFP Plant contributes to the Pb, Cd, As, Hg, Cu, and Cr pollution in nearby soils, particularly Hg pollution. Cu and Cr in soils from both croplands and forestlands in the study area derive mainly from crustal materials or natural processes. Pb, Cd and As in soils from croplands arise partly through anthropogenic activities, but these elements in soils from forestlands originate mainly from crustal materials or natural processes. Hg pollution in soils from both croplands and forestlands is caused mainly by fly ash from the JCFP Plant. The cabbages grown in the study area were severely contaminated with heavy metals, and more than 90% of the cabbages had Pb concentrations exceeding the permissible level established by the Ministry of Health and the Standardization Administration of the People’s Republic of China. Additionally, 30% of the cabbages had As concentrations exceeding the permissible level. Because forests can protect soils from heavy metal pollution caused by atmospheric deposition, close attention should be given to the Hg pollution in soils and to the concentrations of Pb, As, Hg and Cr in vegetables from the study area.

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.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms.