EAGER: Multiscale Modeling of Mechanically-Interlocked Macromolecules

EAGER：机械连锁大分子的多尺度建模

• 批准号: 1912329
• 负责人: Mesfin Tsige
• 金额: $ 19.74万
• 依托单位: University of Akron
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912329&HistoricalAwards=false
• 关键词: EAGER; Multiscale; Modeling; Mechanically; Interlocked

NONTECHNICAL SUMMARYThis award made on an EAGER proposal supports theoretical and computational research and education aimed at advancing fundamental understanding of the physical properties of ring-like molecules interlocked to form a molecular chain called a catanane polymer. The project is focused on investigating the structure and dynamics of polymer catenanes. Mechanically-interlocked macromolecules (MIMs) such as DNA and protein catenanes are macromolecular assemblies that can be thought of as being held together by a kind of "mechanical bond" formed from interlocking rings rather than usual direct chemical bonds. This novel structure is expected to exhibit unique properties, particularly in comparison with those of linear chain-like molecules, classic polymers. Limitations in synthesis approaches have delayed research on MIMs. Since the 1950's improved synthesis methods were sought to substantially increase the yield of MIMs. Recent advances in synthetic methods, particularly "template-directed" synthesis, have substantially improved yields for MIMs and led to the 2016 Nobel prize in Chemistry. This opens research into understanding the unique physical properties of MIMs, as well as their applications.In this project, the PI will use computer simulation in tandem with machine learning (ML) to explore catenated polymers and investigate their structure and dynamics in different physical environments. The PI aims to expand knowledge of the underlying physics inherent in interlocked macromolecules that is critical in exploring the untapped potential of catenated macromolecules for industrial applications. Machine learning approaches will be used to overcome simulation barriers enabling the prediction of new design principles for catenane polymers. In this project, the PI also aims to predict novel target materials for future synthesis, construction and characterization. This project will provide educational experiences for high school students to postdoctoral researchers. High school students from the local St. Vincent-St. Mary school will participate in the proposed work. Undergraduate students will participate through the NSF- REU center at the College of Polymer Science and Polymer Engineering. TECHNICAL SUMMARYThis award made on an EAGER proposal supports theoretical and computational research and education aimed at advancing fundamental understanding of the physical properties of catenated polymers. Mechanically-interlocked macromolecules (MIMs) such as catenanes are macromolecular assemblies held together by topological constraints rather than chemical bonds, possess well-defined topological interactions, and are expected to exhibit a variety of unique properties that are much different than their linear counterparts. Limitations in synthesis approaches has led to slow progress in this area, until the recent development of new synthetic methods, "template-directed" synthesis, which substantially improved yields for MIMs. This project involves the use of all-atom and coarse-grained molecular dynamics simulations in tandem with machine learning (ML) to investigate the structure and dynamics of catenanes, including at surfaces and interfaces. Machine learning approaches will be used to overcome limitations of simulations and enable the prediction of new design principles for catenane polymers. Through theoretical and simulation-based research, the PI aims to expand knowledge of the underlying physics inherent in interlocked macromolecules that is critical in exploring potential industrial applications of catenated macromolecules.This research will provide groundwork for future mesoscale and multiscale modeling of complex polymeric systems. In this project, the PI also aims to predict novel target materials for future synthesis, construction and characterization. The research will provide educational opportunities for students from high school to graduate level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要颁发的渴望提案颁发的奖项支持理论和计算研究和教育，旨在促进对互锁的环形分子的物理特性的基本理解，以形成一种分子链，称为Catanane聚合物。该项目的重点是研究聚合物Catenanes的结构和动力学。机械锁定的大分子（MIMS）（例如DNA和蛋白质蛋白质）是大分子组件，可以将其视为由由互锁环而不是通常的直接化学键形成的一种“机械键”来固定在一起。预计这种新型结构将具有独特的特性，尤其是与线性链样分子（经典聚合物）相比。合成方法的局限性延迟了对MIM的研究。自1950年改善的合成方法以来，寻求大大提高MIM的产量。合成方法的最新进展，尤其是“模板定向”的合成，已大大提高了MIMS的产量，并导致了2016年诺贝尔化学奖。这为理解MIMS及其应用的独特物理特性的研究开放。在本项目中，PI将使用计算机模拟与机器学习（ML）一起探索融合的聚合物并研究其在不同物理环境中的结构和动态。 PI旨在扩大互锁大分子固有的基本物理学的知识，这些物理学对于探索用于工业应用中互联的大分子的未开发潜力至关重要。机器学习方法将用于克服模拟障碍，以预测Catenane聚合物的新设计原理。在该项目中，PI还旨在预测未来合成，构造和表征的新型目标材料。该项目将为高中生提供教育经验。来自当地圣文森特街的高中生。玛丽学校将参加拟议的工作。本科生将通过聚合物科学与聚合物工程学院的NSF-REU中心参加。技术摘要颁发的渴望提出的奖项支持理论和计算研究和教育，旨在促进对融合聚合物物理特性的基本理解。机械交互的大分子（MIMS）（例如Catenanes）是通过拓扑约束而不是化学键合并的大分子组件，具有明确定义的拓扑相互作用，并且有望表现出各种独特的特性，这些特性与线性相反的相反。合成方法的局限性导致该领域的进步缓慢，直到新合成方法的最新发展为“模板定向”的合成，这显着提高了MIMS的产量。该项目涉及在与机器学习（ML）同时使用全原子和粗粒分子动力学模拟来研究Catenanes的结构和动力学，包括在表面和接口处。机器学习方法将用于克服模拟的局限性，并可以预测Catenane聚合物的新设计原理。 通过理论和基于仿真的研究，PI旨在扩大对互锁大分子固有的潜在物理学的了解，这对于探索catenated型大分子的潜在工业应用至关重要。这项研究将为未来的中尺度和复杂聚合物系统的多构想建模提供基础。在该项目中，PI还旨在预测未来合成，构造和表征的新型目标材料。这项研究将为高中到研究生级别的学生提供教育机会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。

项目成果

Spreading Dynamics of Water Droplets on a Completely Wetting Surface

• DOI: 10.1021/acs.jpcc.0c05167
• 发表时间: 2020-09-17
• 期刊: JOURNAL OF PHYSICAL CHEMISTRY C
• 影响因子: 3.7
• 作者: Bekele, Selemon;Evans, Oliver G.;Tsige, Mesfin
• 通讯作者: Tsige, Mesfin

Single Chain Hydration and Dynamics of Mussel-Inspired Soybean-Based Adhesive

• DOI: 10.1007/s11837-021-04756-1
• 发表时间: 2021-06
• 期刊: JOM
• 影响因子: 2.6
• 作者: Abdol Hadi Mokarizadeh;Nityanshu Kumar;Abraham Joy;A. Dhinojwala;M. Tsige

Tuning Solvent Quality Induces Morphological Phase Transitions in Miktoarm Star Polymer Films

• DOI: 10.1021/acs.macromol.0c00770
• 发表时间: 2020-07
• 期刊: Macromolecules
• 影响因子: 5.5
• 作者: Zerihun G. Workineh;G. Pellicane;M. Tsige

Cooperative Multivalent Weak and Strong Interfacial Interactions Enhance the Adhesion of Mussel-Inspired Adhesives

• DOI: 10.1021/acs.macromol.1c00742
• 发表时间: 2021-06
• 期刊: Macromolecules
• 影响因子: 5.5
• 作者: Amal Narayanan;Sukhmanjot Kaur;Nityanshu Kumar;M. Tsige;Abraham Joy;A. Dhinojwala