Enhanced photocatalytic degradation of 17β-estradiol by polythiophene modified Al-doped ZnO: Optimization of synthesis parameters using multivariate optimization techniques

Zinc oxide-based photocatalysts are widely being recognized as efficient materials to degrade emerging organic contaminants, including pharmaceutically active compounds. Previously, various modifications by doping of various metals and non-metals have been incorporated to enhance the performance of zinc oxide. In this work, a novel hybrid photocatalyst was prepared by fabricating zinc oxide with aluminum and polythiophene. Optimization of the synthesis parameters were conducted by targeting the photocatalytic degradation of 17 beta-estradiol under ultraviolet-A irradiation. The prepared catalysts were thoroughly characterized to understand the effect of incorporation of aluminum and polythiophene on the physicochemical properties of the photocatalyst along with its degradation efficiency and kinetics. The experimental data set was generated using a central composite design, which was further modeled and optimized using various multivariate optimization techniques. The influence of individual parameters and their interactive effect on the photocatalyst's performance were analyzed using the outputs of the developed models. Among the employed models, the artificial neural network was found to be the best and was used to generate the optimum conditions (0.47 wt. % polythiophene, 3.14 mol% aluminum and calcination temperature of 174.1 degrees C) for photocatalyst preparation at which a high 17 beta-estradiol removal efficiency of about 96 % was achieved.