A data-driven approach for understanding the structure dependence of redox activity in humic substances.

Humic Substances / analysis Oxidation-Reduction Electrons
Artificial neural network Electron transfer Humic substance Partial least squares regression Redox activity

Journal

Environmental research
ISSN: 1096-0953
Titre abrégé: Environ Res
Pays: Netherlands
ID NLM: 0147621

Informations de publication

Date de publication:
15 02 2023
Historique:
received: 11 11 2022
revised: 03 12 2022
accepted: 17 12 2022
pubmed: 26 12 2022
medline: 18 1 2023
entrez: 25 12 2022
Statut: ppublish

Résumé

Humic substances (HS) can facilitate electron transfer during biogeochemical processes due to their redox properties, but the structure-redox activity relationships are still difficult to describe and poorly understood. Herein, the linear (Partial Least Squares regressions; PLS) and nonlinear (artificial neural network; ANN) models were applied to monitor the structure dependence of HS redox activities in terms of electron accepting (EAC), electron donating (EDC) and overall electron transfer capacities (ETC) using its physicochemical features as input variables. The PLS model exhibited a moderate ability with R

Identifiants

DOI: 10.1016/j.envres.2022.115142 PMID: 36566968
pubmed: 36566968
pii: S0013-9351(22)02469-0
doi: 10.1016/j.envres.2022.115142
pii:
doi:

Substances chimiques

Humic Substances 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

115142

Informations de copyright

Copyright © 2022 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jiajun Ou (J)

School of Automation, Guangdong University of Technology, Guangzhou, 510006, China.

Junlin Wen (J)

Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong University of Technology, Guangzhou, 510006, China; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: jlwen@gdut.edu.cn.

Wenbing Tan (W)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.

Xiaoshan Luo (X)

Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong University of Technology, Guangzhou, 510006, China; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China.

Jiexuan Cai (J)

Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong University of Technology, Guangzhou, 510006, China; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China.

Xiaosong He (X)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.

Lihua Zhou (L)

School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, China.

Yong Yuan (Y)

Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong University of Technology, Guangzhou, 510006, China; School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: yuanyong@soil.gd.cn.

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Classifications MeSH

questionsmedicales.fr › Mélanges complexes › Substances humiques › A data-driven approach for understanding the...
questionsmedicales.fr › Métabolisme › Métabolisme énergétique › Oxydoréduction › A data-driven approach for understanding the...
questionsmedicales.fr › Phénomènes physiques › Phénomènes magnétiques › Phénomènes électromagnétiques › Électrons › A data-driven approach for understanding the...