Fabrication of plasmonic nanoparticles / cobalt doped TiO2 nanosheets for degradation of tetracycline and modeling the process by artificial intelligence techniques

Abstract

The present work was conducted on the synthesis of plasmonic Ag/Ag3PO4 nanoparticles on cobalt doped TiO2 nanosheets (AACoT) as a novel Z-scheme heterostructure photocatalyst. This was performed using deposition-precipitation method and was characterized by variety of technical analyses. We also synthesized and characterized bare Ag/Ag3PO4 (AA), pure TiO2 nanosheets (TNs), Ag/Ag3PO4 on pure TiO2 nanosheets (AAT) and cobalt doped TiO2 nanosheets (CoT) for photocatalytic performance in comparison with AACoT. The prepared photocatalysts were utilized for tetracycline (TC) removal from synthetic wastewater under visible light, where the highest activity among the synthesized samples belonged to AACoT sample. The considerably boosted photocatalytic activity of AACoT could be assigned to the surface plasmon resonance property of Ag nanoparticles, the highly qualified separation of photoproduced electron-hole pairs through the synergistic effect of Ag3PO4 and CoT, and the charge transmission-bridge of Ag nanoparticles. The active species identification demonstrated that O2•- and h+ are the major species involved in the removal of TC we did not observe any significant reduction of photocatalytic performance over AACoT following four successive cycles, which proved an improved stability compared to pure Ag/Ag3PO4. The five operational variables that were investigated for their effect on the photocatalytic performance of TC, included dose of photocatalyst, power of visible light, initial concentration of TC, radiation time and oxidant concentration. Artificial neural network (ANN) and adaptive neuro - fuzzy inference system (ANFIS) were used for optimization and simulation of the degradation process. The designed ANN and ANFIS could exactly anticipate the experimental data with R = 0.99. Moreover, Garson's method was employed for calculation of the relative importance of each parameter.