Xionghui Wei

Dehalogenation of persistent halogenated organic compounds: A review of computational studies and quantitative structure-property relationships.

Dehalogenation is one of the highly important degradation reactions for halogenated organic compounds (HOCs) in the environment, which is also being developed as a potential type of the remediation technologies. In combination with the experimental results, intensive efforts have recently been devoted to the development of efficient theoretical methodologies (e.g. multi-scale simulation) to investigate the mechanisms for dehalogenation of HOCs. This review summarizes the structural characteristics of neutral molecules, anionic species and excited states of HOCs as well as their adsorption behavior on the surface of graphene and the Fe cluster. It discusses the key physiochemical properties (e.g. frontier orbital energies and thermodynamic properties) calculated at various levels of theory (e.g. semiempirical, ab initio, density functional theory (DFT) and the periodic DFT) as well as their connections to the reactivity and reaction pathway for the dehalogenation. This paper also reviews the advances in the linear and nonlinear quantitative structure-property relationship models for the dehalogenation kinetics of HOCs and in the mathematical modeling of the dehalogenation processes. Furthermore, prospects of further expansion and exploration of the current research fields are described in this article.

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.

Electron-induced reductive debromination of 2,3,4-tribromodiphenyl ether: a computational study

To better understand the mechanism of the electron induced elimination of the bromide anion, we examined at the B3LYP/6-31+G(d) level electron capture by 2,3,4-tribromodiphenyl ether (BDE-21) followed by the release of the bromide anion and a radical. Both the geometry and energy of the BDE-21 neutral and its possible anionic states were studied. A significant relationship was found between the total energy and the length of the C-Br bonds by the analysis of the potential energy surface for the anionic states. Debromination preference for the bromine substituted positions was theoretically evaluated as meta-Br > ortho-Br > para-Br. The reaction profiles of the electron-induced debromination of BDE-21 demonstrated that, in general, the presence of a solvent makes the electron induced reductive debromination of BDE-21 significantly more advantageous, and the stabilization effect of the solvent on the reaction intermediates would make the electron attachment and dissociation relatively effective in comparison with the results from the gas-phase calculations.

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.

Theoretical study on the radical anions and reductive dechlorination of selected polychlorinated dibenzo-p-dioxins.

For the effective use of remediation technologies for PCDDs contamination, it is essential to study the reactivity and dechlorination pathways of these compounds. In this study, density functional theory (DFT) calculations (B3LYP/6-31+G(d), B3LYP/6-311+G(d,p)) were performed to investigate the neutrals and different anionic states of selected PCDD congeners. The calculated adiabatic electron affinities and frontier orbital energies of the PCDD congeners (in gas-phase and in solution) are significantly correlated with the reported dechlorination rate constants, showing that this kind of reductive cleavage reaction is kinetically controlled by the electron transfer step. The predicted major dechlorination pathways of 1,2,3,4-TeCDD and its daughter products based on the energies of the anionic states were found to be satisfactorily consistent with the reported experimental results. Simulation of the 1,2,3,4-TeCDD dechlorination process showed that not only the dechlorination regioselectivity but also the reactivity of the PCDDs played an important role in the distribution of dechlorinated products. An exponential correlation was found between the sum of the concentration of the PCDD congeners and the reaction time in the simulation, indicating that the time required for the conversion of the PCDD congeners to the fully dechlorinated product (dibenzo-p-dioxin) might not be significantly dependent on the initial concentration of 1,2,3,4-TeCDD.

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.

Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process

Reduced-graphene-oxide-supported bimetallic Fe/Ni nanoparticles were synthesized in this study for the removal of crystal violet (CV) dye from aqueous solutions. This material was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS). The influence of independent parameters (namely, initial dye concentration, initial pH, contact time, and temperature) on the removal efficiency were investigated via Box–Behnken design (BBD). Artificial intelligence (i.e., artificial neural network, genetic algorithm, and particle swarm optimization) was used to optimize and predict the optimum conditions and obtain the maximum removal efficiency. The zero point of charge (pHZPC) of rGO/Fe/Ni composites was determined by using the salt addition method. The experimental equilibrium data were fitted well to the Freundlich model for the evaluation of the actual behavior of CV adsorption, and the maximum adsorption capacity was estimated as 2000.00 mg/g. The kinetic study discloses that the adsorption processes can be satisfactorily described by the pseudo-second-order model. The values of Gibbs free energy change (ΔG0), entropy change (ΔS0), and enthalpy change (ΔH0) demonstrate the spontaneous and endothermic nature of the adsorption of CV onto rGO/Fe/Ni composites.