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 |
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