Density Functional Theory and Machine Learning-Based Quantitative Structure–Activity Relationship Models Enabling Prediction of Contaminant Degradation Performance with Heterogeneous Peroxymonosulfate Treatments-Reference-Cited by-同舟云学术

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

Published:2023-02-22 Issue:9 Volume:57 Page:3951-3961
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Xiao Zijie¹^ORCID,Yang Bowen¹,Feng Xiaochi¹^ORCID,Liao Zhenqin¹,Shi Hongtao¹,Jiang Weiyu¹,Wang Caipeng¹,Ren Nanqi¹

Affiliation:

1. 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

Funder

State Key Laboratory of Urban Water Resource and Environment

Science, Technology and Innovation Commission of Shenzhen Municipality

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.2c09034

Reference59 articles.