Growing Co-doped TiO2 nanosheets on reduced graphene oxide for efficient photocatalytic removal of tetracycline antibiotic from aqueous solution and modeling the process 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.