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Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation

Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG–ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG–ANG to attain the advanced research projects agency‐energy (ARPA‐E) target for onboard methane sto...

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Autores principales: Kim, Seo‐Yul, Han, Seungyun, Lee, Seulchan, Kang, Jo Hong, Yoon, Sunghyun, Park, Wanje, Shin, Min Woo, Kim, Jinyoung, Chung, Yongchul G., Bae, Youn‐Sang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313482/
https://www.ncbi.nlm.nih.gov/pubmed/35524582
http://dx.doi.org/10.1002/advs.202201559
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author Kim, Seo‐Yul
Han, Seungyun
Lee, Seulchan
Kang, Jo Hong
Yoon, Sunghyun
Park, Wanje
Shin, Min Woo
Kim, Jinyoung
Chung, Yongchul G.
Bae, Youn‐Sang
author_facet Kim, Seo‐Yul
Han, Seungyun
Lee, Seulchan
Kang, Jo Hong
Yoon, Sunghyun
Park, Wanje
Shin, Min Woo
Kim, Jinyoung
Chung, Yongchul G.
Bae, Youn‐Sang
author_sort Kim, Seo‐Yul
collection PubMed
description Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG–ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG–ANG to attain the advanced research projects agency‐energy (ARPA‐E) target for onboard methane storage has not been fully investigated. In this work, large‐scale computational screening is performed for 5446 metal–organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm(3)(STP) cm(−3)) are identified. Furthermore, structure–performance relationships are realized under the LNG–ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT‐23(Cu) and DUT‐23(Co) show higher working capacities (≈373 cm(3)(STP) cm(−3)) than that of any other porous material under ANG or LNG–ANG conditions, and excellent stability during cyclic LNG–ANG operation.
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spelling pubmed-93134822022-07-27 Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation Kim, Seo‐Yul Han, Seungyun Lee, Seulchan Kang, Jo Hong Yoon, Sunghyun Park, Wanje Shin, Min Woo Kim, Jinyoung Chung, Yongchul G. Bae, Youn‐Sang Adv Sci (Weinh) Research Articles Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG–ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG–ANG to attain the advanced research projects agency‐energy (ARPA‐E) target for onboard methane storage has not been fully investigated. In this work, large‐scale computational screening is performed for 5446 metal–organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm(3)(STP) cm(−3)) are identified. Furthermore, structure–performance relationships are realized under the LNG–ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT‐23(Cu) and DUT‐23(Co) show higher working capacities (≈373 cm(3)(STP) cm(−3)) than that of any other porous material under ANG or LNG–ANG conditions, and excellent stability during cyclic LNG–ANG operation. John Wiley and Sons Inc. 2022-05-07 /pmc/articles/PMC9313482/ /pubmed/35524582 http://dx.doi.org/10.1002/advs.202201559 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Kim, Seo‐Yul
Han, Seungyun
Lee, Seulchan
Kang, Jo Hong
Yoon, Sunghyun
Park, Wanje
Shin, Min Woo
Kim, Jinyoung
Chung, Yongchul G.
Bae, Youn‐Sang
Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation
title Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation
title_full Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation
title_fullStr Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation
title_full_unstemmed Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation
title_short Discovery of High‐Performing Metal–Organic Frameworks for On‐Board Methane Storage and Delivery via LNG–ANG Coupling: High‐Throughput Screening, Machine Learning, and Experimental Validation
title_sort discovery of high‐performing metal–organic frameworks for on‐board methane storage and delivery via lng–ang coupling: high‐throughput screening, machine learning, and experimental validation
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313482/
https://www.ncbi.nlm.nih.gov/pubmed/35524582
http://dx.doi.org/10.1002/advs.202201559
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