Retention time prediction of emerging contaminants via transfer learning with graph neural networks.
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| Title: | Retention time prediction of emerging contaminants via transfer learning with graph neural networks. |
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| Authors: | Deng J; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China., Chen J; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China., Wang J; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China., Li X; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China., Liu S; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China., Ge L; School of Environmental Science and Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China., Ying GG; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China., Chen CE; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China. Electronic address: changer.chen@m.scnu.edu.cn. |
| Source: | Journal of hazardous materials [J Hazard Mater] 2026 Feb 15; Vol. 504, pp. 141313. Date of Electronic Publication: 2026 Jan 31. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9422688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3336 (Electronic) Linking ISSN: 03043894 NLM ISO Abbreviation: J Hazard Mater Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 41621299 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Retention time prediction of emerging contaminants via transfer learning with graph neural networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Deng+J%22">Deng J</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China.<br /><searchLink fieldCode="AU" term="%22Chen+J%22">Chen J</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China.<br /><searchLink fieldCode="AU" term="%22Wang+J%22">Wang J</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China.<br /><searchLink fieldCode="AU" term="%22Li+X%22">Li X</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China.<br /><searchLink fieldCode="AU" term="%22Liu+S%22">Liu S</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China.<br /><searchLink fieldCode="AU" term="%22Ge+L%22">Ge L</searchLink>; School of Environmental Science and Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China.<br /><searchLink fieldCode="AU" term="%22Ying+GG%22">Ying GG</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China.<br /><searchLink fieldCode="AU" term="%22Chen+CE%22">Chen CE</searchLink>; Environmental Research Institute/School of Environment, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China. Electronic address: changer.chen@m.scnu.edu.cn. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229422688%22">Journal of hazardous materials</searchLink> [J Hazard Mater] 2026 Feb 15; Vol. 504, pp. 141313. <i>Date of Electronic Publication: </i>2026 Jan 31. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%22">Elsevier </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>9422688 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1873-3336 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2203043894%22">03043894 </searchLink><i>NLM ISO Abbreviation: </i>J Hazard Mater <i>Subsets: </i>MEDLINE; PubMed not MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41621299 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jhazmat.2026.141313 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 141313 Titles: – TitleFull: Retention time prediction of emerging contaminants via transfer learning with graph neural networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Deng J – PersonEntity: Name: NameFull: Chen J – PersonEntity: Name: NameFull: Wang J – PersonEntity: Name: NameFull: Li X – PersonEntity: Name: NameFull: Liu S – PersonEntity: Name: NameFull: Ge L – PersonEntity: Name: NameFull: Ying GG – PersonEntity: Name: NameFull: Chen CE IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: 2026 Feb 15 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1873-3336 Numbering: – Type: volume Value: 504 Titles: – TitleFull: Journal of hazardous materials Type: main |
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