Improved Norfloxacin degradation by urea precipitation Ti/SnO2–Sb anode under photo-electro catalysis and kinetics investigation by BP-neural-network-physical modeling

Abstract

Abstract The photo-electro catalysis has emerged as efficient and sustainable degradation method for antibiotics, where metal oxide anode plays a critical role. Exploring novel preparation method for anodes catalysis to achieve larger active sites and diverse oxidants production can directly enhance the degradation performance. Therefore, in this work, a new urea precipitation method for anode formation was studied. Both novel urea precipitation Ti/SnO2–Sb anode (TSSA-U) and traditional electro-deposition Ti/SnO2–Sb anode (TSSA-E) were prepared for Norfloxacin degradation by photo-electro catalysis in saline water. A unique tubular Ti/SnO2–Sb units formatted by urea precipitation resulted in higher porosity. This led to dominating advantage for TSSA-U on photo/electrochemical activity and degradation performances by individual photo/electro catalysis, compared to TSSA-E. However, this gap of degradation performances shrank when synergistic effect of photo-electro catalysis involved. The highest TOC removal ratio of 91.1% was obtained from TSSA-U under photo-electro catalysis. Besides, a novel BP-neural-network-physical modeling (BP-ANN-P) was developed for analysis. According to this modeling, both direct (adsorption-degradation, radiation, etc.) and indirect (mainly •Cl) routes contributed significantly in degradation work by TSSA-U, where indirect route shared 41.8–90.1% of total degradation ratio. An increasing of current density (from 5 to 25\xa0mA\xa0cm−2) enhanced the kinetics for both routes. Indirect route preferred pH\xa0=\xa03–7 with direct route enhanced by neutral condition. Moreover, indirect route also showed better adaptability with higher initial NOR loadings. Both urea precipitation and BP-ANN-P have shown their value for high performance material formation and data analysis, respectively. Notably, higher catalysis performance and better analysis connect to low energy cost, shorter running time and more efficient judgement and selection, which meets the requirement of cleaner production and environmental sustainability.