Machine Learning-Based Design of Ionic Liquids at the Atomic Scale for Highly Efficient CO<sub>2</sub> Capture-Reference-Cited by-同舟云学术

Machine Learning-Based Design of Ionic Liquids at the Atomic Scale for Highly Efficient CO₂ Capture

Published:2023-06-01 Issue:24 Volume:11 Page:8978-8987
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Liu Xiangyang¹,Chu Jianchun¹,Huang Shaoxuan¹,Li An¹,Wang Shuanlai¹,He Maogang¹^ORCID

Affiliation:

1. Key Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shaanxi Province

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.3c01191

Reference53 articles.

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5. Low-Energy-Consumption CO2 Capture by Liquid–Solid Phase Change Absorption Using Water-Lean Blends of Amino Acid Salts and 2-Alkoxyethanols

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