Predicting CO<sub>2</sub> Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks-Reference-Cited by-同舟云学术

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Predicting CO₂ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks

Published:2022-12-02 Issue:50 Volume:10 Page:16681-16691
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Jian Yue¹^ORCID,Wang Yuyang²³^ORCID,Barati Farimani Amir¹²³^ORCID

Affiliation:

1. Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

2. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

3. Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

Funder

Advanced Research Projects Agency - Energy

Carnegie Mellon University

Publisher

American Chemical Society (ACS)

Subject

Renewable Energy, Sustainability and the Environment,General Chemical Engineering,Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acssuschemeng.2c05985

Reference72 articles.

1. A study of methods of carbon dioxide capture and sequestration––the sustainability of a photosynthetic bioreactor approach

2. IPCC. Carbon Dioxide Capture and Storage. https://www.ipcc.ch/report/carbon-dioxide-capture-and-storage/ (accessed November 11, 2022).

3. Physical Solubility and Diffusivity of N2O and CO2 into Aqueous Solutions of (2-Amino-2-methyl-1-propanol + Monoethanolamine) and (N-Methyldiethanolamine + Monoethanolamine)

4. A 13C NMR study of the carbon dioxide absorption and desorption equilibria by aqueous 2-aminoethanol and N-methyl-substituted 2-aminoethanol

5. State-of-the-art Adsorption and Membrane Separation Processes for Carbon Dioxide Production from Carbon Dioxide Emitting Industries

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