Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning-Reference-Cited by-同舟云学术

Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning

Published:2024 Issue: Volume: Page:
ISSN:2754-7000
Container-title:Environmental Science: Advances
language:en
Short-container-title:Environ. Sci.: Adv.

Author:

Fan Yunwu¹,Deng Yu¹,Yang Yi¹,Deng Xin¹,Li Qianhui¹,Xu Boqi¹,Pan Jianyu¹,Liu Sisi¹²^ORCID,Kong Yan³,Chen Chang-Er¹²^ORCID

Affiliation:

1. School of Environment, MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China

2. Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, South China Normal University, Guangzhou 510006, China

3. Women and Children's Hospital, Qingdao University, Qingdao 266000, China

Abstract

Machine learning is increasingly popular and promising in environmental science due to its potential in solving various environmental problems, particularly with simple code-free tools.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

South China Normal University

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://pubs.rsc.org/en/content/articlepdf/2024/VA/D3VA00242J

Reference44 articles.

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