量子化学模拟在抗生素高级氧化降解中的跨尺度融合
PDF下载 (53)李 兵,朱威明,余俊新,朱晓晖,古天宇,王 英,董志颖,蒋 丽.量子化学模拟在抗生素高级氧化降解中的跨尺度融合[J].宁波大学学报(理工版),2026,39(1):69-83.DOI:10.20098/j.cnki.1001-5132.2025.0524
Li Bing,Zhu Weiming,Yu Junxin,Zhu Xiaohui,Gu Tianyu,Wang Ying,Dong Zhiying,Jiang Li.Quantum chemistry simulation of cross-scale fusion in
the advanced oxidative degradation of antibiotics
[J].Journal of Ningbo University(Natural Science & Engineering Edition),2026,39(1):69-83.DOI:10.20098/j.cnki.1001-5132.2025.0524
| Title: | Quantum chemistry simulation of cross-scale fusion in
the advanced oxidative degradation of antibiotics |
| 作者: | 李 兵, 朱威明, 余俊新, 朱晓晖, 古天宇, 王 英, 董志颖, 蒋 丽 |
| Author(s): | Li Bing, Zhu Weiming, Yu Junxin, Zhu Xiaohui, Gu Tianyu, Wang Ying, Dong Zhiying, Jiang Li |
| 关键词: | 抗生素; 高级氧化; 量子化学模拟; 量子化学描述符; 活性物种; 催化剂 |
| Keywords: | antibiotics; advanced oxidation; quantum chemistry simulation; quantum chemistry descriptors; active species; catalyst |
| 分类号: | X705 |
| DOI: | 10.20098/j.cnki.1001-5132.2025.0524 |
| 文献标识码: | A |
| 摘要: | 抗生素作为新兴污染物，因其持久性、生物活性及抗性基因扩散风险，对环境和人体健康构成严重威胁。传统污水处理工艺对抗生素去除效果有限，促使高级氧化技术（AOPs）成为研究热点，而量子化学模拟能为AOPs降解抗生素路径和机理研究提供新方法。本文综述了量子化学模拟融合在抗生素降解路径预测、活化机理解析及催化剂设计中的应用进展，重点探讨了量子化学描述符的作用。综合分析表明，量子化学模拟能够精准识别抗生素分子的活性位点，预测反应路径，阐明活化物种机制，结合相关检测手段可验证并明晰各种催化剂的电子结构与界面效应。未来须融合机器学习构建“模拟预判-实验验证-定向调控”框架，通过动态模拟预测降解中间体毒性演化，结合多尺度表征数据推动催化剂靶向开发与AOPs体系生态风险防控，突破传统试错模式，实现污染防治技术智能化升级。 |
| Abstract: | Antibiotics, as emerging contaminants, present significant environmental and human health risks owing to their persistence, biological activity, and potential to spread resistance genes. Conventional wastewater treatment processes exhibit limited efficacy in antibiotic removal, driving advanced oxidation processes (AOPs) to emerge as a research hotspot. Quantum chemical simulations offer a transformative approach for studying the AOP-mediated antibiotic degradation pathways and mechanisms. This review comprehensively examines the integrated application of quantum chemical simulations in antibiotic degradation pathway prediction, activation mechanism elucidation, and catalyst design optimization, with a focus on the roles of quantum chemical descriptors. Integrated analyses indicate that quantum chemical simulations can precisely identify the reactive sites of antibiotic molecules, predict degradation pathways, and elucidate the mechanisms of reactive species. Combined with experimental characterization techniques, these simulations can validate and clarify the electronic structures and interfacial effects of catalysts. Future efforts should focus on integrating machine learning to establish a “simulation prediction-experimental verification-directional regulation” framework. Dynamic simulations could predict toxicity evolution of degradation intermediates, while multiscale characterization data would facilitate catalyst development and ecological risk management of AOPs. This paradigm shift aims to transcend traditional trial-and-error approaches and achieve intelligent upgrading of contamination prevention-and-control technologies. |
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| 备注/Memo: | 收稿日期：2025-05-26 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ 基金项目：浙江省基础公益研究计划（LTGS24E080001） 第一作者：李 兵，博士/副教授，主要研究方向为固体废物处理处置与资源化。E-mail: libing@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
