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

ABSTRACT: Reduced graphene oxide-supported Fe?O? (Fe?O?/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 Fe?O?/rGO composites were prepared by the solvothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), N?-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 Fe?O?/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 Fe?O?/rGO composites demonstrated that these composites can be reused for the removal of low-concentration mercury from aqueous solutions.

SUBMITTER: Cao R

PROVIDER: S-EPMC5706226 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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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.

Cao Rensheng R Fan Mingyi M Hu Jiwei J Ruan Wenqian W Xiong Kangning K Wei Xionghui X

Materials (Basel, Switzerland) 20171107 11

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 transfo ...[more]

PMID: 29112141

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Project description:Industrial pollution by heavy metal ions such as Hg2+ and Ag⁺ is a universal problem owing to the toxicity of heavy metals. In this study, a novel nano-adsorbent, i.e., poly-2-mercapto-1,3,4-thiadiazole (PTT), was synthesized and used to selectively adsorb mercury and silver ions from aqueous solutions. PTT nanoparticles were synthesized via chemical oxidative dehydrogenation polymerization under mild conditions. Oxidant species, medium, monomer concentration, oxidant/monomer molar ratio, and polymerization temperature were optimized to obtain optimum yields. The molecular structure and morphology of the nanoparticles were analyzed by ultraviolet-visible (UV-Vis), Fourier transform infrared (FT-IR), matrix-assisted laser desorption/ionization/time-of-flight (MALDI/TOF) mass and X-ray photoelectron (XPS) spectroscopies, wide-angle X-ray diffraction (WAXD), theoretical calculations and transmission electron microscopy (TEM), respectively. It was found that the polymerization of 2-mercapto-1,3,4-thiodiazole occurs through head-to-tail coupling between the S(2) and C(5) positions. The PTT nanoparticles having a peculiar synergic combination of four kinds of active groups, S⁻, ⁻SH, N⁻N, and =N⁻ with a small particle size of 30⁻200 nm exhibit ultrarapid initial adsorption rates of 1500 mg(Hg)·g-1·h-1 and 5364 mg(Ag)·g-1·h-1 and high adsorption capacities of up to 186.9 mg(Hg)·g-1 and 193.1 mg(Ag)·g-1, becoming ultrafast chelate nanosorbents with high adsorption capacities. Kinetic study indicates that the adsorption of Hg2+ and Ag⁺ follows the pseudo-second-order model, suggesting a chemical adsorption as the rate-limiting step during the adsorption process. The Hg2+ and Ag⁺-loaded PTT nanoparticles could be effectively regenerated with 0.1 mol·L-1 EDTA or 1 mol·L-1 HNO₃ without significantly losing their adsorption capacities even after five adsorption⁻desorption cycles. With these impressive properties, PTT nanoparticles are very promising materials in the fields of water-treatment and precious metals recovery.

Dataset Information

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

Publications

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.

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