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

Seo Yul Kim, Seungyun Han, Seulchan Lee, Jo Hong Kang, Sunghyun Yoon, Wanje Park, Min Woo Shin, Jinyoung Kim, Yongchul G. Chung, Youn Sang Bae

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

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 cm3(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 cm3(STP) cm−3) than that of any other porous material under ANG or LNG–ANG conditions, and excellent stability during cyclic LNG–ANG operation.

Original languageEnglish
Article number2201559
JournalAdvanced Science
Volume9
Issue number21
DOIs
Publication statusPublished - 2022 Jul 25

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • General Chemical Engineering
  • General Materials Science
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • General Engineering
  • General Physics and Astronomy

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this