Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1784754090551541760 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9313482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kimseoyul discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT hanseungyun discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT leeseulchan discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT kangjohong discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT yoonsunghyun discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT parkwanje discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT shinminwoo discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT kimjinyoung discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT chungyongchulg discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation AT baeyounsang discoveryofhighperformingmetalorganicframeworksforonboardmethanestorageanddeliveryvialngangcouplinghighthroughputscreeningmachinelearningandexperimentalvalidation |