Density Functional Theory and Machine Learning-Based Quantitative Structure-Activity Relationship Models Enabling Prediction of Contaminant Degradation Performance with Heterogeneous Peroxymonosulfate Treatments.

Xiao, Zijie; Yang, Bowen; Feng, Xiaochi; Liao, Zhenqin; Shi, Hongtao; Jiang, Weiyu; Wang, Caipeng; Ren, Nanqi

Xiao, Zijie; Yang, Bowen; Feng, Xiaochi; Liao, Zhenqin; Shi, Hongtao; Jiang, Weiyu; Wang, Caipeng; Ren, Nanqi.

Afiliação

Xiao Z; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Yang B; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Feng X; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Liao Z; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Shi H; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Jiang W; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Wang C; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.
Ren N; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China.

Environ Sci Technol ; 57(9): 3951-3961, 2023 03 07.

Article em En | MEDLINE | ID: mdl-36809928

Assuntos

Peróxidos; Relação Quantitativa Estrutura-Atividade; Teoria da Densidade Funcional; Aprendizado de Máquina

Palavras-chave

density functional theory; heterogeneous activation; machine learning; oxidation pathway; quantitative structure−activity relationship

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peróxidos / Relação Quantitativa Estrutura-Atividade Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2023 Tipo de documento: Article País de publicação: Estados Unidos

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