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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.

A one-pot hydrothermal synthesis was applied for growth of cobalt-doped TiO2 nanosheets (Co-TNs) having different quantities of cobalt on the reduced graphene oxide surfaces (Co-TNs/rGO (x)). The synthesized nanocomposites were characterized by a range of analyses including XRD, UV–Vis DRS, FESEM/EDX, elemental mapping, TEM, HRTEM and Raman spectroscopy. The visible light degradation of Tetracycline antibiotic (TC) by synthesized samples was investigated and the degradation percentage of TC was 60% by Co-TNs/rGO (0.152) (the optimal sample). Reusing the optimal photocatalyst after five successive cycles showed ∼7% decline in its activity for degrading of TC. Active species trapping experiments showed that OH radicals and h+ are the main active species in the degradation process. An artificial neural network (ANN) model was used to predict the photocatalytic removal of tetracycline antibiotic. The multilayered feed forward networks were trained by using a backpropagation algorithm; a three-layer network with 14 neurons in the hidden layer gave the optimal results. The relative importance of different parameters on the photoactivity of the as-obtained photocatalysts were evaluated.