Machine Learning-Driven Discovery of Metal–Organic Frameworks for Efficient CO2 Capture in Humid Condition-Reference-Cited by-同舟云学术

Machine Learning-Driven Discovery of Metal–Organic Frameworks for Efficient CO2 Capture in Humid Condition

Published:2021-02-05 Issue:7 Volume:9 Page:2872-2879
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Zhang Xiangyu¹,Zhang Kexin¹²³,Yoo Hyeonsuk⁴,Lee Yongjin¹⁴^ORCID

Affiliation:

1. School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China

2. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201203, China

3. University of Chinese Academy of Sciences, Beijing 100049, China

4. Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea

Funder

National Natural Science Foundation of China

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.0c08806

Reference52 articles.

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2. Pilot Plant Studies of the CO2 Capture Performance of Aqueous MEA and Mixed MEA/MDEA Solvents at the University of Regina CO2 Capture Technology Development Plant and the Boundary Dam CO2 Capture Demonstration Plant

3. Membrane technologies for CO2 separation

4. Advances in high permeability polymer-based membrane materials for CO2 separations

5. CO2 capture by solid adsorbents and their applications: current status and new trends

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