Construction of graphene based photocatalysts for photocatalytic degradation of organic pollutant and modeling using artificial intelligence techniques

In this study, graphene based photocatalysts containing different silver doped titanium dioxide nanosheets (AgTNs-RGO) were fabricated. The successful reduction of graphene oxide (as support) was confirmed by Raman analysis. Also, powder X-ray diffraction (PXRD) analysis confirmed pure anatase phase of the obtained TiO2 nanosheets as well as Raman spectroscopy. The efficiency of Ag-TNs-RGO (0.038) for degradation of tetracycline (TC) antibiotic as an organic pollutant model under visible light and UV irradiation for 180 min was evaluated 48.57% and 77.84%, respectively. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to describe the photocatalytic degradation process. Five degradation variables (catalyst dosage, initial concentration of TC, silver nitrate content, visible light irradiation time and ammonium persulfate (APS) concentration) were defined as the input variables of the models and the removal percentage of TC was the output. Sensitivity analysis was performed by weights method on the outputs of models.

Automated summary

In this study, graphene based photocatalysts with different silver doped titanium dioxide nanosheets were fabricated and their efficiency in degrading tetracycline antibiotic was evaluated. Artificial neural network and adaptive neuro-fuzzy inference system were used to describe the photocatalytic degradation process, with sensitivity analysis performed on the outputs of the models.