Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(<scp>ii</scp>) and Cd(<scp>ii</scp>) ions-Reference-Cited by-同舟云学术

Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb₂CT_x towards Pb(ii) and Cd(ii) ions

Published:2023 Issue:16 Volume:11 Page:9009-9018
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Jaffari Zeeshan Haider¹,Abbas Ather¹,Umer Muhammed²,Kim Eun-Sik³,Cho Kyung Hwa¹⁴^ORCID

Affiliation:

1. School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan 44919, Republic of Korea

2. Department of Chemistry, Center for Superfunctional Materials, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan 44919, Republic of Korea

3. Department of Environmental System Engineering, Chonnam National University, Daehak-ro 50, Yeosu 59626, Republic of Korea

4. Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan 44919, Republic of Korea

Abstract

Precisely measuring the adsorption capability of materials towards toxic heavy metal ions in aqueous solution is essential for the synthesis of effective novel adsorbents.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D3TA00019B

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1. Preliminarily comparative performance of removing bisphenol-S by ferrate oxidation and ozonation

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