催化作用
降级(电信)
污染物
异质结
甲基橙
亚甲蓝
化学
化学工程
激进的
电子顺磁共振
材料科学
纳米技术
光化学
有机化学
光催化
光电子学
电信
物理
核磁共振
计算机科学
工程类
作者
Yong Wang,Shishi Shen,Mingyue Liu,Guangyu He,Xibao Li
标识
DOI:10.1016/j.jcis.2023.10.164
摘要
Tribocatalysis research, leveraging the triboelectric effect, presents significant potential for environmental water pollution control. However, there is a notable scarcity of studies pertaining to tribocatalysis involving heterojunctions, particularly in the context of p-n junction tribocatalysis. In this study, we employed a one-step solvothermal method to synthesize a Cu1.8S/CuCo2S4 p-n junction composite catalyst. Subsequently, we explored the tribocatalytic degradation performance of organic pollutants facilitated by the Cu1.8S/CuCo2S4 catalyst. The findings reveal that, under simple magnetic stirring conditions, the degradation rates achieved by the Cu1.8S/CuCo2S4 catalyst for tetracycline (TC), methylene blue (MB), and methyl orange (MO) are remarkably high, reaching 99.9 %, 99.7 %, and 94.0 %, respectively. This underscores the broad applicability of the Cu1.8S/CuCo2S4 catalyst for the tribocatalytic degradation of diverse organic pollutants. Experimental evidence establishes that friction occurring between the polytetrafluoroethylene (PTFE) magnet rod, the beaker, and the catalyst induces charge transfer at their interfaces, generating highly oxidized active species that effectively decompose pollutants. Through free radical capture and electron spin resonance (ESR) tests, it was empirically determined and validated that the principal active species involved in tribocatalytic degradation are holes (h+) and superoxide radicals (O2–). Incorporating insights from the experimental characterization of p-n junctions and density functional theory (DFT) theoretical calculations, we propose a plausible tribocatalytic mechanism for Cu1.8S/CuCo2S4. This research not only contributes novel findings but also serves as a reference for the exploration of innovative heterojunction tribocatalysts.
科研通智能强力驱动
Strongly Powered by AbleSci AI