基于密度泛函理论和机器学习的双功能树脂-微生物复合物设计用于增强苯酚降解和Cr (VI)还原

IF 7.2 2区 工程技术 Q1 ENGINEERING, CHEMICAL
Yan Hai, Qiyao Cong, Yingnan Pang, Yunxing Zhao, Weilun Yan, Jianfeng Zhang, Jing Liang
{"title":"基于密度泛函理论和机器学习的双功能树脂-微生物复合物设计用于增强苯酚降解和Cr (VI)还原","authors":"Yan Hai,&nbsp;Qiyao Cong,&nbsp;Yingnan Pang,&nbsp;Yunxing Zhao,&nbsp;Weilun Yan,&nbsp;Jianfeng Zhang,&nbsp;Jing Liang","doi":"10.1016/j.jece.2025.119308","DOIUrl":null,"url":null,"abstract":"<div><div>Hexavalent chromium and phenol coexist in wastewater and exhibit synergistic toxicity, enhancing biological harmful effects. The integration of Density Functional Theory (DFT) and Machine Learning (ML) offers a promising approach to addressing complex environmental challenges, through bridging macroscopic and microscopic regular analysis. DFT simulations revealed that the tertiary amine (-N(CH<sub>3</sub>)<sub>2</sub>) and quaternary ammonium (-N<sup>+</sup>(CH<sub>3</sub>)<sub>3</sub>) groups on D301, carrying positive charges, can assemble phenol-degrading microorganisms through electrostatic interactions. HOMO/LUMO energy and the Fukui function revealed a low energy gap of 0.128 Ha between D301 and Cr(VI), suggesting the potential for spontaneous Cr(VI) reduction by D301. experiments demonstrated that the resin–microorganism composite material could degrade 1500 mg/L of phenol and reduce 20 mg/L of Cr(VI), which is higher than most of the currently reported co-removal levels. Using Bayesian regression, a synergistic metabolic model is established to predict the removal performance. The resin-microbe system can remove 34–36 % of 1800 mg/L phenol under 20 mg/L Cr(VI), with a prediction error of less than 5 %. This study, through DFT and integrated ML, revealed the active sites of the resin and constructed a co-metabolism model of phenol and Cr(VI), providing a new strategy for material design and microbial assembly in the removal of co-contaminants.</div></div>","PeriodicalId":15759,"journal":{"name":"Journal of Environmental Chemical Engineering","volume":"13 6","pages":"Article 119308"},"PeriodicalIF":7.2000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of bifunctional resin-microbe complex guided by density functional theory and machine learning for enhanced phenol degradation and Cr (VI) reduction\",\"authors\":\"Yan Hai,&nbsp;Qiyao Cong,&nbsp;Yingnan Pang,&nbsp;Yunxing Zhao,&nbsp;Weilun Yan,&nbsp;Jianfeng Zhang,&nbsp;Jing Liang\",\"doi\":\"10.1016/j.jece.2025.119308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Hexavalent chromium and phenol coexist in wastewater and exhibit synergistic toxicity, enhancing biological harmful effects. The integration of Density Functional Theory (DFT) and Machine Learning (ML) offers a promising approach to addressing complex environmental challenges, through bridging macroscopic and microscopic regular analysis. DFT simulations revealed that the tertiary amine (-N(CH<sub>3</sub>)<sub>2</sub>) and quaternary ammonium (-N<sup>+</sup>(CH<sub>3</sub>)<sub>3</sub>) groups on D301, carrying positive charges, can assemble phenol-degrading microorganisms through electrostatic interactions. HOMO/LUMO energy and the Fukui function revealed a low energy gap of 0.128 Ha between D301 and Cr(VI), suggesting the potential for spontaneous Cr(VI) reduction by D301. experiments demonstrated that the resin–microorganism composite material could degrade 1500 mg/L of phenol and reduce 20 mg/L of Cr(VI), which is higher than most of the currently reported co-removal levels. Using Bayesian regression, a synergistic metabolic model is established to predict the removal performance. The resin-microbe system can remove 34–36 % of 1800 mg/L phenol under 20 mg/L Cr(VI), with a prediction error of less than 5 %. This study, through DFT and integrated ML, revealed the active sites of the resin and constructed a co-metabolism model of phenol and Cr(VI), providing a new strategy for material design and microbial assembly in the removal of co-contaminants.</div></div>\",\"PeriodicalId\":15759,\"journal\":{\"name\":\"Journal of Environmental Chemical Engineering\",\"volume\":\"13 6\",\"pages\":\"Article 119308\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Environmental Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213343725040047\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Environmental Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213343725040047","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

摘要

六价铬和苯酚在废水中共存,并表现出协同毒性,增强了生物有害效应。密度泛函理论(DFT)和机器学习(ML)的整合通过连接宏观和微观的常规分析,为解决复杂的环境挑战提供了一种有前途的方法。DFT模拟结果表明,D301上的叔胺(-N(CH3)2)和季铵(-N+(CH3)3)基团携带正电荷,可通过静电相互作用组装苯酚降解微生物。HOMO/LUMO能量和Fukui函数显示D301和Cr(VI)之间存在0.128 Ha的低能差,表明D301有可能自发还原Cr(VI)。实验表明,树脂-微生物复合材料可降解1500 mg/L苯酚,降低20 mg/L Cr(VI),高于目前报道的大多数共去除水平。利用贝叶斯回归,建立了协同代谢模型来预测去除效果。在20 mg/L Cr(VI)下,树脂-微生物体系对1800 mg/L苯酚的去除率可达34-36 %,预测误差小于5 %。本研究通过DFT和集成ML揭示了树脂的活性位点,构建了苯酚和Cr(VI)的共代谢模型,为材料设计和微生物组装去除共污染物提供了新的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of bifunctional resin-microbe complex guided by density functional theory and machine learning for enhanced phenol degradation and Cr (VI) reduction
Hexavalent chromium and phenol coexist in wastewater and exhibit synergistic toxicity, enhancing biological harmful effects. The integration of Density Functional Theory (DFT) and Machine Learning (ML) offers a promising approach to addressing complex environmental challenges, through bridging macroscopic and microscopic regular analysis. DFT simulations revealed that the tertiary amine (-N(CH3)2) and quaternary ammonium (-N+(CH3)3) groups on D301, carrying positive charges, can assemble phenol-degrading microorganisms through electrostatic interactions. HOMO/LUMO energy and the Fukui function revealed a low energy gap of 0.128 Ha between D301 and Cr(VI), suggesting the potential for spontaneous Cr(VI) reduction by D301. experiments demonstrated that the resin–microorganism composite material could degrade 1500 mg/L of phenol and reduce 20 mg/L of Cr(VI), which is higher than most of the currently reported co-removal levels. Using Bayesian regression, a synergistic metabolic model is established to predict the removal performance. The resin-microbe system can remove 34–36 % of 1800 mg/L phenol under 20 mg/L Cr(VI), with a prediction error of less than 5 %. This study, through DFT and integrated ML, revealed the active sites of the resin and constructed a co-metabolism model of phenol and Cr(VI), providing a new strategy for material design and microbial assembly in the removal of co-contaminants.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Environmental Chemical Engineering
Journal of Environmental Chemical Engineering Environmental Science-Pollution
CiteScore
11.40
自引率
6.50%
发文量
2017
审稿时长
27 days
期刊介绍: The Journal of Environmental Chemical Engineering (JECE) serves as a platform for the dissemination of original and innovative research focusing on the advancement of environmentally-friendly, sustainable technologies. JECE emphasizes the transition towards a carbon-neutral circular economy and a self-sufficient bio-based economy. Topics covered include soil, water, wastewater, and air decontamination; pollution monitoring, prevention, and control; advanced analytics, sensors, impact and risk assessment methodologies in environmental chemical engineering; resource recovery (water, nutrients, materials, energy); industrial ecology; valorization of waste streams; waste management (including e-waste); climate-water-energy-food nexus; novel materials for environmental, chemical, and energy applications; sustainability and environmental safety; water digitalization, water data science, and machine learning; process integration and intensification; recent developments in green chemistry for synthesis, catalysis, and energy; and original research on contaminants of emerging concern, persistent chemicals, and priority substances, including microplastics, nanoplastics, nanomaterials, micropollutants, antimicrobial resistance genes, and emerging pathogens (viruses, bacteria, parasites) of environmental significance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信