Molecular dynamic simulation and artificial neural network (ANN) modeling of the functionalized graphene oxide membranes on Cr (VI) ion removal through electrodialysis method

Abstract

In this study, the separation of chromium (VI) ion as a dangerous heavy metal material from water by graphene oxide (GO) membrane was investigated using molecular simulation method. Pores were created in the middle of GO membrane and then the membrane pores were functionalized by adding -F, -H, and -OH chemical groups. The external voltages were applied to the studied system to improve the efficiency of Cr (VI) and water separation process. To study the functionalized GO membrane performance under applied voltage in the separation process, analyzes were performed, which include permeation, artificial neural network (ANN), radial distribution function, hydrogen bond, ion tracking path, water density map and diffusion coefficient of ion. The outcomes demonstrate that the GO membrane can be constructed with an adequate functional porosity and a sufficient applied voltage, and that it performs well throughout the heavy metal separation procedure.