Prof. Jiwei Hu

Prof. Jiwei Hu |Clyto Access

Guizhou Normal University, China


Expertise: synthesis and characterization of functional nanomaterials


Prof. Jiwei Hu is currently a Distinguished Professor of environmental science at Cultivation Base of Guizhou National Key Laboratory of Mountainous Karst Eco-environment, Guizhou Normal University. He has received his B.S. degree in chemical engineering from Zhejiang Institute of Technology in 1984, his M.S. degree in organic chemistry from the University of Jyväskylä in 1994, his Licentiate degree in environmental chemistry from Stockholm University in 1996, and his Ph.D. in applied chemistry from the University of Jyväskylä in 1999. He carried out his research at College of Chemistry and Molecular Engineering, Peking University, as a Post-Doctoral Fellow from 2002 to 2005. His main research areas are as follows: (1) synthesis and characterization of functional nanomaterials; (2) removal of organic and inorganic pollutants in wastewater; (3) application of artificial intelligence in experimental design; (4) natural products research; (5) molecular modeling and quantitative structure-activity relationships. He has co-authored approximately 170 conference and journal papers and has so far obtained 7 China patents.



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


Rhodamine B (RhB) 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/rGOcomposites 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. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology(RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second-order kinetic model.p>,

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