{"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, Qiyao Cong, Yingnan Pang, Yunxing Zhao, Weilun Yan, Jianfeng Zhang, 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, Qiyao Cong, Yingnan Pang, Yunxing Zhao, Weilun Yan, Jianfeng Zhang, 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}
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.
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.