Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations-Reference-Cited by-同舟云学术

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Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

Published:2024-03-29 Issue:14 Volume:128 Page:6089-6105
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Daou Alan S. S.¹,Fang Hanjun¹^ORCID,Boulfelfel Salah Eddine¹,Ravikovitch Peter I.²,Sholl David S.¹³^ORCID

Affiliation:

1. School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States

2. ExxonMobil Technology and Engineering Company, 1545 Route 22 East, Annandale, New Jersey 08801, United States

3. Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c00066

Reference134 articles.

1. Natural Gas

2. Natural Gas

3. Use of Light Alkane Fingerprints in Attributing Emissions from Oil and Gas Production

4. Greater focus needed on methane leakage from natural gas infrastructure

5. Gas separation by adsorption: technological drivers and opportunities for improvement

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1. Simulation and Machine Learning-Integrated Investigation of Double-Linker MOFs for the Separation of CF₄ from CH₄ and N₂;Industrial & Engineering Chemistry Research;2024-07-03

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