Photocatalytic treatment of tetracycline antibiotic wastewater by silver/TiO2 nanosheets/reduced graphene oxide and artificial neural network modeling

In this work, we focused on facile preparation of ternary nanocomposites containing TiO2 nanosheets, reduced graphene oxide, and different quantities of silver for photocatalytic treatment of tetracycline (TC) antibiotic wastewater. Plasmonic silver nanoparticles were deposited on TiO2 nanosheets/reduced graphene oxide nanocomposite via a photodeposition method (TGA(x) samples). The as-obtained samples were identified by variety of techniques such as XRD, UV-Vis DRS, FESEM/EDX, TEM, and Raman spectroscopy. The photocatalytic degradation experiments of TC (in concentration of 30 mg/L) were carried out by synthesized nanocomposites, and the degradation efficiency of TGA (0.076) (the optimal sample) was evaluated as 52.56% after 3 hr of irradiation under visible light. The obtained results showed that in TGA(x) samples, the reduced graphene oxide acts as a bridge for transferring photoinduced electrons from plasmonic silver nanoparticles to TiO2 nanosheetes. A three-layered artificial neural network model with four input variables (irradiation time, catalyst dosage, initial concentration of TC, and silver nitrate content) and one output variable (% degradation) was optimized with 11 hidden neurons. The relative importance of the independent variables was calculated using Garson formula and the initial concentration of TC was found as the most influencing parameter (relative importance of 31%) on the treatment efficiency. Practitioner points TiO2 nanosheets synthesized on the reduced graphene oxide by hydrothermal method. Plasmonic silver nanoparticles deposited on TNs/rGO by photodeposition method The photocatalytic degradation of tetracycline (TC) was modeled by artificial neural network.