Tailored transition metal-doped TiO 2 @Fe 3 O 4 nanohybrids for efficient photocatalytic dye removal: Optimization via neural networks and response surface approaches

The study focused on predicting the degradation of Remazol Turquoise Blue (RTB) dye using a photocatalyst comprised of transition metal -doped TiO 2 @Fe 3 O 4 . Out of the transition metal ions considered, namely Cu, Cr, Co, and Ni, Cr emerges as the most effective dopant for TiO 2 @Fe 3 O 4 nanohybrid photocatalysts. This optimal performance was achieved with a catalyst loading of 50 mg and a dye concentration of 10 ppm within a 1-h reaction time, resulting in a photocatalytic efficiency of 96.70%. The optimization strategy employed a combination of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). To conduct experiments systematically, a Box-Behnken Design (BBD) rooted in RSM principles was utilized to create experimental setups for the photocatalytic degradation of the dye with the doped photocatalyst. The variables such as reaction time, catalyst dosage and dye concentration were designed to optimize the RTB dye degradation. The Regression coefficient (R 2 ) for the developed RSM and ANN models were found to be 0.9299 and 0.9925 respectively, and revealed that the ANN has higher prediction capability and accuracy than other model. The outcomes underlined that the dosage of the catalyst prominently emerged as the principal determinant governing the efficacy of the photocatalytic activity. Remarkably, the Cr doped TiO 2 @Fe 3 O 4 photocatalyst maintained its activity through six degradation cycles, with efficiencies of 99.7%, 99.4%, 99.1%, 88.8%, 87.3%, and 86.3%, respectively.