Machine learning strategy on activation energy of environmental heterogeneous reactions and its application to atmospheric formation of typical montmorillonite-bound phenoxy radicals.

Pan, Wenxiao; Chang, Jiamin; He, Shuming; Liu, Xian; Fu, Jianjie; Zhang, Aiqian

Pan, Wenxiao; Chang, Jiamin; He, Shuming; Liu, Xian; Fu, Jianjie; Zhang, Aiqian.

Afiliação

Pan W; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
Chang J; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China.
He S; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China.
Liu X; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China. Electronic address: xia
Fu J; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, PR China
Zhang A; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China; School of Environment,

Sci Total Environ ; 895: 165117, 2023 Oct 15.

Article em En | MEDLINE | ID: mdl-37364832

Palavras-chave

Activation energy prediction; Heterogeneous reaction; Long-lived free radical formation; Machine learning framework

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2023 Tipo de documento: Article

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