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Computational Approaches for the Design and Screening of Novel Biopolymers
Computational Approaches for the Design and Screening of Novel Biopolymers
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151011
- ISBN
- 9798382759081
- DDC
- 660
- 저자명
- Shebek, Kevin.
- 서명/저자
- Computational Approaches for the Design and Screening of Novel Biopolymers
- 발행사항
- [Sl] : Northwestern University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 127 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Broadbelt, Linda J.;Tyo, Keith E. J.
- 학위논문주기
- Thesis (Ph.D.)--Northwestern University, 2024.
- 초록/해제
- 요약Reliance on polymers produced from traditional fossil fuel feedstocks has led to an increase in greenhouse gas emissions and long-lived waste in the environment. Biopolymers offer an attractive solution to this problem owing to their sustainable production, biocompatibility, biodegradability, and tunability. However, the design space of these polymers is vast and difficult to explore. In this dissertation, a high-throughput computational screening method is used to design novel biopolymers and pathways for their synthesis. The first portion of the thesis develops computational tools necessary to create and analyze biopolymers. First, Pickaxe, an automated reaction network generation tool, is developed. This tool utilizes reaction rules to generate synthesis pathways from a set of starting compounds into a novel set of compounds. The next section of the thesis details the development of PolyIDRS, a machine learning prediction tool for polymer properties. PolyIDRS is the first tool of its kind to utilize stereochemistry in polymers, which is ubiquitous in biopolymers and is essential for the design of novel materials. In the final portion of this dissertation, these tools are combined to produce a computational workflow to identify novel polyhydroxyalkanoates, which are an attractive biopolymer target due to their tunability and biodegradability. Combining Pickaxe and PolyIDRS allows for the generation of novel biopolymers to replace traditional polymers as well as the design of more sustainable synthesis pathways for existing polymers. Collectively, this dissertation demonstrates the utility of combining computational tools to generate high-throughput screening methods to discover the next generation of biopolymers.
- 일반주제명
- Chemical engineering
- 일반주제명
- Polymer chemistry
- 일반주제명
- Computational chemistry
- 키워드
- Machine learning
- 키워드
- Biopolymers
- 키워드
- Stereochemistry
- 키워드
- Biodegradability
- 기타저자
- Northwestern University Chemical and Biological Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382759081
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aShebek, Kevin.
■24510▼aComputational Approaches for the Design and Screening of Novel Biopolymers
■260 ▼a[Sl]▼bNorthwestern University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a127 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Broadbelt, Linda J.;Tyo, Keith E. J.
■5021 ▼aThesis (Ph.D.)--Northwestern University, 2024.
■520 ▼aReliance on polymers produced from traditional fossil fuel feedstocks has led to an increase in greenhouse gas emissions and long-lived waste in the environment. Biopolymers offer an attractive solution to this problem owing to their sustainable production, biocompatibility, biodegradability, and tunability. However, the design space of these polymers is vast and difficult to explore. In this dissertation, a high-throughput computational screening method is used to design novel biopolymers and pathways for their synthesis. The first portion of the thesis develops computational tools necessary to create and analyze biopolymers. First, Pickaxe, an automated reaction network generation tool, is developed. This tool utilizes reaction rules to generate synthesis pathways from a set of starting compounds into a novel set of compounds. The next section of the thesis details the development of PolyIDRS, a machine learning prediction tool for polymer properties. PolyIDRS is the first tool of its kind to utilize stereochemistry in polymers, which is ubiquitous in biopolymers and is essential for the design of novel materials. In the final portion of this dissertation, these tools are combined to produce a computational workflow to identify novel polyhydroxyalkanoates, which are an attractive biopolymer target due to their tunability and biodegradability. Combining Pickaxe and PolyIDRS allows for the generation of novel biopolymers to replace traditional polymers as well as the design of more sustainable synthesis pathways for existing polymers. Collectively, this dissertation demonstrates the utility of combining computational tools to generate high-throughput screening methods to discover the next generation of biopolymers.
■590 ▼aSchool code: 0163.
■650 4▼aChemical engineering
■650 4▼aPolymer chemistry
■650 4▼aComputational chemistry
■653 ▼aMachine learning
■653 ▼aBiopolymers
■653 ▼aStereochemistry
■653 ▼aBiodegradability
■653 ▼aSustainable production
■690 ▼a0542
■690 ▼a0800
■690 ▼a0219
■690 ▼a0495
■71020▼aNorthwestern University▼bChemical and Biological Engineering.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0163
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160401▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
