Synthesis and characterization of BaAl2O4: Ce and Mn-Ce-co-doped BaAl2O4 composite materials by a modified polyacrylamide gel method and prediction of photocatalytic activity using artificial neural network (ANN) algorithm

BaAl2O4:Ce and Mn-Ce-co-doped BaAl2O4 composite materials were synthesized with barium nitrate, cerium trichloride hexahydrate, aluminum trichloride hydrate and manganese acetate tetrahydrate as the raw materials by a gamma-ray irradiation assisted polyacrylamide gel method. The doping of Ce or Ce and Mn ions with BaAl2O4 improves the surface morphology, optical, photoluminescence, electrochemical properties and photocatalytic activities of the matrix material, but did not change the crystal structure of the matrix material. BaAl2O4:Ce:Mn composite materials exhibits high light absorption capacity, charge transfer capacity and separation efficiency, and photocatalytic activity for photocatalytic degradation of methylene blue dye under simulated sunlight irradiation. Based on the study of the effects of catalyst content, dye concentration, pH value and irradiation time on photocatalytic activity for the BaAl2O4:Ce:Mn composite materials, an artificial neural network model was established with Matlab software to simulate its photocatalytic performance. The results show that the neural network algorithm has potential applications in the simulation of photocatalytic activity of semiconductor materials.