Synthesis and Characterization of Fe3O4/TiO2/Ag Magnetic Nanocomposite with Enhanced Photocatalytic Activity for Methylene Blue Degradation and Modeling by an Artificial Neural Network (ANN)

Increasing environmental pollution is one of the major problems in recent decades. Finding new ways to remove contaminants is critical mission for scientists. In this research, Fe3O4/TiO2/Ag magnetic nanocomposite synthesized for investigation of degradation of methylene blue (MB). Fe3O4 magnetic nanoparticles was first synthesized with simple co-precipitation method. Then the magnetic nanocomposite structure of Fe3O4/TiO2 by hydrothermal methodwas shaped. After that, to improve the ability of the nanocomposite to reduction of MB, Ag nanoparticles was doped on the surface of the Fe3O4/TiO2. In fact, in this structure, we used local surface plasmon resonance (LSPR) future of Ag and photocatalyst property of TiO2 to modify the ability of MB reduction. Various techniques were employed to characterize the morphology of magnetic nanocomposite such as X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM) and an alternating gradient force magnetometer (AGFM). We also used ultraviolet-visible (UV) analyses to determine the band gap. The results show that the nanocomposite formed successfully in desired structure and morphology. Catalytic measurements on the samples show an excellent efficiency for the MBdegradation. After the reduction of MB, one can use a magnet bar to separate the catalyst from solution easily. Artificial neural network (ANN) models can eliminate the huge part of experimental investigations in various filed of science and technology. After gathering some information about the methyl blue degradation, the ANN modeling was carried out to calculate the optimum values of initial variables to achieve the maximum removal efficiency. In this project, we used an initial ion concentration, the amount of nanocomposite that were used in photocatalyst activity and removal time as initial variables, finally the removal efficiency of pollution (MB) was considered as the output. In this project, we used a genetic algorithm (GA) to trained models and predation.