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Low-Temperature Phenomena in Aqueous Systems
Low-Temperature Phenomena in Aqueous Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152936
- ISBN
- 9798346759027
- DDC
- 660
- 서명/저자
- Low-Temperature Phenomena in Aqueous Systems
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 113 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Debedetti, Pablo G.;Panagiotopoulos, Athanassios Z.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약Water, the most abundant liquid on earth, is also one of the least understood. The existence of a liquid-liquid critical point (LLCP) in the deeply-supercooled regime is a leading explanation for many of them. The molecular mechanisms underlying these behaviors occur at length and time scales which are often inaccessible experimentally. This work examines them with large-scale, atomistic molecular dynamics simulations. It explores how microscopic properties such as bond flexibility and polarizability in water and amino acid sequences of proteins influence such macroscopic properties as ice crystallization and the LLCP.An LLCP is rigorously located in WAIL, a water model parameterized using ab-initio calculations only, and incorporating realistic bond flexibility and polarizability. The existence of a critical point in WAIL provides strong support to the view that the LLCP is a robust feature in the free energy landscape of supercooled water. Previous models shown to contain an LLCP did not permit bond flexion or polarization despite their known importance.Classical nucleation theory is used to compute the homogeneous nucleation rate of ice Ih in the TIP4P/Ice model at conditions ranging from ambient to the vicinity of the LLCP. Supercooling was found to be the dominant influence on nucleation rate, but at high supercoolings the Widom line causes the appearance of a locus of maxima with regard to pressure. The Widom line affects nucleation rates primarily through the ice-liquid surface tension. Recent advances in protein structure prediction have made possible a larger reference dataset and more general genetic algorithm for optimization of antifreeze proteins (AFPs) than that used by Kozuch et al. A neural network trained on the expanded AFP data set and used to optimize four AFPs predicts significant increases in thermal hysteresis. The binding surface is also demonstrated.Many questions remain about supercooled water. The LLCP can only be definitively shown with experiments. The quantitative description of nucleation in TIP4P/Ice contains approximations and empiricisms. Synthesizing and purifying proteins with high predicted thermal hysteresis and incorporating them into the reference dataset would more clearly define what is possible with AFPs, as would incorporating data on ice-nucleating proteins.
- 일반주제명
- Chemical engineering
- 일반주제명
- Molecular physics
- 일반주제명
- Computational chemistry
- 일반주제명
- Thermodynamics
- 키워드
- Crystallization
- 키워드
- Ice
- 키워드
- Machine learning
- 키워드
- Nucleation
- 키워드
- Water
- 기타저자
- Princeton University Chemical and Biological Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152936
■006m o d
■007cr#unu||||||||
■020 ▼a9798346759027
■035 ▼a(MiAaPQ)AAI31563746
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aWeis, Jack Charles.▼0(orcid)0000-0002-9430-0762
■24510▼aLow-Temperature Phenomena in Aqueous Systems
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a113 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Debedetti, Pablo G.;Panagiotopoulos, Athanassios Z.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aWater, the most abundant liquid on earth, is also one of the least understood. The existence of a liquid-liquid critical point (LLCP) in the deeply-supercooled regime is a leading explanation for many of them. The molecular mechanisms underlying these behaviors occur at length and time scales which are often inaccessible experimentally. This work examines them with large-scale, atomistic molecular dynamics simulations. It explores how microscopic properties such as bond flexibility and polarizability in water and amino acid sequences of proteins influence such macroscopic properties as ice crystallization and the LLCP.An LLCP is rigorously located in WAIL, a water model parameterized using ab-initio calculations only, and incorporating realistic bond flexibility and polarizability. The existence of a critical point in WAIL provides strong support to the view that the LLCP is a robust feature in the free energy landscape of supercooled water. Previous models shown to contain an LLCP did not permit bond flexion or polarization despite their known importance.Classical nucleation theory is used to compute the homogeneous nucleation rate of ice Ih in the TIP4P/Ice model at conditions ranging from ambient to the vicinity of the LLCP. Supercooling was found to be the dominant influence on nucleation rate, but at high supercoolings the Widom line causes the appearance of a locus of maxima with regard to pressure. The Widom line affects nucleation rates primarily through the ice-liquid surface tension. Recent advances in protein structure prediction have made possible a larger reference dataset and more general genetic algorithm for optimization of antifreeze proteins (AFPs) than that used by Kozuch et al. A neural network trained on the expanded AFP data set and used to optimize four AFPs predicts significant increases in thermal hysteresis. The binding surface is also demonstrated.Many questions remain about supercooled water. The LLCP can only be definitively shown with experiments. The quantitative description of nucleation in TIP4P/Ice contains approximations and empiricisms. Synthesizing and purifying proteins with high predicted thermal hysteresis and incorporating them into the reference dataset would more clearly define what is possible with AFPs, as would incorporating data on ice-nucleating proteins.
■590 ▼aSchool code: 0181.
■650 4▼aChemical engineering
■650 4▼aMolecular physics
■650 4▼aComputational chemistry
■650 4▼aThermodynamics
■653 ▼aAntifreeze proteins
■653 ▼aCrystallization
■653 ▼aIce
■653 ▼aMachine learning
■653 ▼aNucleation
■653 ▼aWater
■690 ▼a0542
■690 ▼a0609
■690 ▼a0219
■690 ▼a0348
■71020▼aPrinceton University▼bChemical and Biological Engineering.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164232▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
