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Accurate Predictions of the Adsorption Space and Efficient Sorbent Discovery in Metal-Organic Frameworks
Accurate Predictions of the Adsorption Space and Efficient Sorbent Discovery in Metal-Organic Frameworks
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105528
- ISBN
- 9798263343880
- DDC
- 548.73
- 저자명
- Yu, Xiaohan.
- 서명/저자
- Accurate Predictions of the Adsorption Space and Efficient Sorbent Discovery in Metal-Organic Frameworks
- 발행사항
- [Sl] : Georgia Institute of Technology, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 261 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Sholl, David.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2025.
- 초록/해제
- 요약Adsorption-based separations using metal-organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. The present thesis addresses this limitation by first developing accurate ML models predicting Henry's constants and heats of adsorption. We then developed accurate ML models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. By combining molecular simulation data, ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures. We then focused on exploring MOFs for direct air capture (DAC). We presented Open DAC(ODAC) 2023 database with over 38 million quantum chemistry calculations on thousands of MOFs containing CO2 and/or H2O. We introduced a tool to automatically generate missing-linker defects in MOFs and applied the tool to include more than three thousand defective MOFs to the database. Over two hundreds of promising MOFs were identified and the influence of defects was studied. Machine learning models were developed based on this database to accelerate the development of MOFs for DAC.
- 일반주제명
- Point defects
- 일반주제명
- Adsorption
- 일반주제명
- Atomic physics
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263343880
■035 ▼a(MiAaPQ)AAI32309791
■035 ▼a(MiAaPQ)GeorgiaTech77813
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a548.73
■1001 ▼aYu, Xiaohan.
■24510▼aAccurate Predictions of the Adsorption Space and Efficient Sorbent Discovery in Metal-Organic Frameworks
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a261 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Sholl, David.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2025.
■520 ▼aAdsorption-based separations using metal-organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. The present thesis addresses this limitation by first developing accurate ML models predicting Henry's constants and heats of adsorption. We then developed accurate ML models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. By combining molecular simulation data, ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures. We then focused on exploring MOFs for direct air capture (DAC). We presented Open DAC(ODAC) 2023 database with over 38 million quantum chemistry calculations on thousands of MOFs containing CO2 and/or H2O. We introduced a tool to automatically generate missing-linker defects in MOFs and applied the tool to include more than three thousand defective MOFs to the database. Over two hundreds of promising MOFs were identified and the influence of defects was studied. Machine learning models were developed based on this database to accelerate the development of MOFs for DAC.
■590 ▼aSchool code: 0078.
■650 4▼aPoint defects
■650 4▼aAdsorption
■650 4▼aAtomic physics
■690 ▼a0800
■690 ▼a0748
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360449▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
