A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces-Reference-Cited by-同舟云学术

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A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces

Published:2023-08 Issue: Volume:155 Page:105731
ISSN:0883-2927
Container-title:Applied Geochemistry
language:en
Short-container-title:Applied Geochemistry

Author:

Chang Elliot^ORCID,Zavarin Mavrik,Beverly Linda,Wainwright Haruko

Funder

Lawrence Livermore National Laboratory

Lawrence Berkeley National Laboratory

U.S. Department of Energy

Biological and Environmental Research

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Pollution,Environmental Chemistry

Reference62 articles.

1. Sorption of actinides in well-defined oxidation states on geologic media;Allard;MRS Online Proc. Libr.,2011

2. Error propagation by the Monte Carlo method in geochemical calculations;Anderson;Geochem. Cosmochim. Acta,1976

3. Surface complexation of ferrous iron and carbonate on ferrihydrite and the mobilization of arsenic;Appelo;Environ. Sci. Technol.,2002

4. Sorption of uranium(VI) onto phyllite;Arnold;Chem. Geol.,1998

5. Sorption behavior of U(VI) on phyllite: experiments and modeling;Arnold;J. Contam. Hydrol.,1999

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model;Water Research;2024-06

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