化学
二氧化钛
介质阻挡放电
催化作用
降级(电信)
水溶液
光催化
化学工程
核化学
有机化学
电极
计算机科学
电信
工程类
物理化学
作者
Jingwei Feng,Luqing Pan,Huiyuan Liu,Shengtao Yuan,Liu Zhang,Hao Yin,Hao Song,Liya Li
标识
DOI:10.1016/j.seppur.2022.121761
摘要
The wide band gap of titanium dioxide (TiO2) photocatalyst leads to its narrow optical response, which seriously affects the application of TiO2. In order to enhance its catalytic efficiency, defective TiO2-exposed {0 0 1} facets (TiO2-x) was prepared and applied to pollutants degradation by combing with the dielectric barrier discharge nonthermal plasma (DBD-NTP), so as to use the ultraviolet light and other active species produced by DBD-NTP. Taking aqueous norfloxacin (NOR) as a model pollutant, the degradation performance of NOR by DBD-NTP and TiO2-x was evaluated, the synergistic catalytic mechanism and degradation pathways of NOR based on heterogeneous catalysis were proposed. An artificial neural network model based on genetic algorithm optimization (GA-ANN) was constructed to predict the NOR degradation efficiency. The characteristics of TiO2-x were characterized, and the effects of influencing factors on the NOR degradation efficiency were investigated. The results showed a significant synergistic catalytic effect in NOR degradation by DBD-NTP combined with TiO2-x. The synergistic catalytic effect substantially increased with increasing TiO2-x dosage, and the synergistic factor increased by 3.3 times. The reactive agents 1O2, OH, O2−, h+ and e− played an essential role in NOR degradation, with the contribution of 1O2 being the most significant. TiO2-x had good catalytic stability. The degradation pathways of NOR mainly included breakage of the piperazine ring and quinoline ring, decarboxylation, benzene ring defluorination and mineralization. The established GA-ANN model showed high accuracy (mean square error = 0.0016, correlation coefficient = 0.9956) and could be used to predict the degradation efficiency of aqueous NOR.
科研通智能强力驱动
Strongly Powered by AbleSci AI