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Zhu, Tengyi, Tao, Cuicui (2022) Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. Journal of Hazardous Materials, 423. 127037pp. doi:10.1016/j.jhazmat.2021.127037

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Reference TypeJournal (article/letter/editorial)
TitlePrediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor
JournalJournal of Hazardous Materials
AuthorsZhu, TengyiAuthor
Tao, CuicuiAuthor
Year2022 (February)Volume423
PublisherElsevier BV
DOIdoi:10.1016/j.jhazmat.2021.127037Search in ResearchGate
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Mindat Ref. ID13665909Long-form Identifiermindat:1:5:13665909:1
GUID0
Full ReferenceZhu, Tengyi, Tao, Cuicui (2022) Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. Journal of Hazardous Materials, 423. 127037pp. doi:10.1016/j.jhazmat.2021.127037
Plain TextZhu, Tengyi, Tao, Cuicui (2022) Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor. Journal of Hazardous Materials, 423. 127037pp. doi:10.1016/j.jhazmat.2021.127037
In(2022) Journal of Hazardous Materials Vol. 423. Elsevier BV

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Tao, Cuicui, Chen, Ying, Tao, Tianyun, Cao, Zaizhi, Chen, Wenxuan, Zhu, Tengyi (2022) Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature. Environmental Pollution, 311. 119857 doi:10.1016/j.envpol.2022.119857
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Zhu, Tengyi; Yang, Hengguang; Tao, Cuicui; Li, Yi; Li, Shuyin; Chen, Xiangyu (2025) Revealing aqueous adsorption mechanisms of organic contaminants and optimizing prediction models from the molecular structure scale. Materials Chemistry and Physics, 345. doi:10.1016/j.matchemphys.2025.131234
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Li, Yi, Tao, Cuicui, Fu, Dafang, Jafvert, Chad T., Zhu, Tengyi (2024) Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds. Chemosphere, 349. 140984 doi:10.1016/j.chemosphere.2023.140984
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