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绘制抗生素光催化转化和耐药性风险与DFT信息机器学习工作流程的映射
Chen-Chen Zhao1, Sihan Xing1, Cheng Fu1
1State Key Laboratory of Coordination Chemistry, School of Chemistry, Nanjing University, Nanjing, Jiangsu, 210023, P.R. China.
Angewandte Chemie (International ed. in English)
|January 20, 2026
概括
抗生素的光催化降解可以产生危险的转化产品 (TP). 这项研究开发了一个使用机器学习和DFT的预测框架,以评估TP风险,识别高风险物种并优先考虑可持续的污染物降解.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 生态毒理学 生态毒理学
背景情况:
- 光催化降解有效地去除抗生素,但可以产生具有生态风险的转化产品 (TP),包括抗生素耐药性基因 (ARG) 诱导.
- 评估与这些TP相关的风险对于确保安全和可持续的水处理至关重要.
研究的目的:
- 开发一个预测框架来评估在光催化降解过程中产生的抗生素转化产品 (TPs) 的生态风险.
- 整合实验数据,计算建模和机器学习来预测TP特性并优先考虑可持续降解途径.
主要方法:
- 将光催化实验与高分辨率质谱,密度函数理论 (DFT) 计算和机器学习 (ML) 模型相结合.
- 构建广泛的反应网络并使用自动过渡状态搜索来评估动力可访问性.
- 开发一个多维评分系统 (DEBF),整合多样性,生态毒性,生物降解性和可行性,以优先考虑降解途径.
主要成果:
- 为四环素降解构建了一个由超过9533个反应组成的反应网络,并训练了一种ML模型以准确地预测Gibbs自由能量变化.
- 该框架的通用性在五种不同的抗生素中得到验证,涉及545种反应.
- 鉴于增强的ARG结合潜力,几种氧化,氨基化和胺基TP被确定为高风险.
结论:
- 开发的框架为分析光催化转换提供了一个可概括的,基于机制的,并具有风险意识的方法.
- 这种方法可以推导出污染物降解的设计原则,使效率与生态安全保持平衡.
- 该研究强调了在光催化过程中考虑TP风险对环境保护的重要性.
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