DMREF: Tuning Liquid Crystallinity in Conjugated Polymers to Simultaneously Enhance Charge Transport and Control Mechanical Properties

DMREF：调节共轭聚合物的液晶性，同时增强电荷传输并控制机械性能

• 批准号: 1921854
• 负责人: Enrique Gomez
• 金额: $ 175万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921854&HistoricalAwards=false
• 关键词: DMREF; Tuning; Liquid; Crystallinity; Conjugated

Non-technical Description: The proposed computationally-guided approach will provide an accelerated materials design framework useful for both academic and industrial efforts to accelerate the development of conjugated polymers for flexible electronics. The work is designed to leverage progress in the prediction and measurement of fundamental properties of conjugated polymers, and move forward along the materials development continuum. Key efforts integrate theory, simulations, and experiments, both through the development of new tools and by refining concepts of how microstructure governs charge transport in conjugated polymers. Furthermore, the Principal Investigators will develop an ambitious outreach pilot program that uses research activities as tools for improving educational opportunities and outcomes for students at non-PhD institutions (such as community colleges). Penn State is a unique microcosm of the broader higher education ecosystem, because it consists of a central research-intensive campus that is integrated with 19 largely two-year commonwealth campuses serving more diverse student populations - making Penn State an ideal incubator to explore the use of research as a recruiting and retention tool. In collaboration with the Leonhard Center for the Enhancement of Engineering Education at Penn State, the proposed work will establish a data-driven program to translate computational tools from the proposed technical objectives into web-based research experiences targeting Science, Technology, Engineering, and Mathematics (STEM) students at Penn State commonwealth campuses. Technical Description: The work within this proposal leverages previous advances to predict the persistence length, glass transition temperature and nematic-to-isotropic transition temperature. The proposed project aims to further advance computational materials design, by developing tools capable of accelerating the prediction of mechanical and conductive properties. Three computational tools will be developed: coarse-grained models based on force-matching to accelerate computational design of liquid crystalline semiflexible polymers, chain-shrinking simulations to predict the effect of liquid crystallinity on entanglement, and tight-binding models to explore the role of packing and disorder on charge transport. The combination of simulations and experiments will be crucial to generate accurate coarse-grained simulations capable of predicting liquid crystallinity through the Principal Investigators' approach that combines molecular dynamics simulations with self-consistent field theory calculations. This will enable the systematic computational exploration of backbone and side chain architectures that are validated with selected synthesized model materials. Simulations and experiment will also be crucial to incorporate nematic order in the Principal Investigators' unified theory of polymer entanglements, and thereby provide a tool capable of predicting rheological properties (e.g. mechanical properties) of conjugated polymers from the chemical structure. Furthermore, tight-binding models will predict the role of packing and local disorder on charge transport, to explore the hypotheses that layered disordered phases can play a crucial role in promoting efficient charge transport by facilitating pi-stacking. Such models will be validated by measurements of the charge mobility as a function of temperature and within various crystalline, liquid crystalline, or isotropic phases.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.

非技术描述：拟议的计算指导方法将提供一个加速的材料设计框架，可用于学术和工业努力，以加速柔性电子产品的共轭聚合物的开发。这项工作旨在利用共轭聚合物的基本特性的预测和测量方面的进步，并沿着材料开发连续性前进。关键努力通过开发新工具的开发以及完善微观结构如何控制共轭聚合物中电荷运输的概念来整合理论，模拟和实验。此外，首席研究人员将开发一项雄心勃勃的外展试验计划，该计划使用研究活动作为改善非PHD机构（例如社区学院）学生的教育机会和成果的工具。宾夕法尼亚州立大学是更广泛的高等教育生态系统的独特缩影，因为它由一个中央研究密集型校园组成，该校园与19个基本上为期两年的英联邦校园融合在一起，这些校园为更多的学生人群提供服务 - 使宾夕法尼亚州成为一个理想的孵化器，以探索研究作为招聘和保留工具的研究。与宾夕法尼亚州立大学（Penn State）的工程教育增强工程教育中心合作，拟议的工作将建立一个数据驱动的计划，将计算工具从拟议的技术目标转化为针对宾夕法尼亚州州联邦校园科学，技术，工程和数学（STEM）学生的基于Web的基于Web的研究经验。技术描述：此提案中的工作利用了以前的进步来预测持久性长度，玻璃过渡温度和列主至异位过渡温度。拟议的项目旨在通过开发能够加速机械和导电性能预测的工具来进一步推进计算材料设计。将开发三种计算工具：基于力匹配的粗粒模型，以加速液体晶体晶体半串联聚合物的计算设计，链脱链模拟，以预测液体结晶性对纠缠的影响以及探索填料和无序在电荷运输中的作用的紧密结合模型。模拟和实验的组合对于生成能够通过主要研究者的方法来预测液晶度的准确的粗粒模拟至关重要，该模拟将分子动力学模拟与自洽场理论计算相结合。这将使通过选定的合成模型材料验证的骨干和侧链体系结构进行系统的计算探索。模拟和实验对于将列表纳入主要研究者的聚合物纠缠理论，因此提供了一种能够预测化学结构中共轭聚合物的流变特性（例如机械性能）的工具。此外，紧密结合模型将预测包装和局部障碍在电荷运输中的作用，以探索分层无序相位可以通过促进PI堆叠来促进有效的电荷运输的假设。此类模型将通过测量电荷迁移率作为温度的函数以及在各种晶体，液晶或各向同性阶段的函数来验证。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优点和更广泛影响的审查标准来评估的。

项目成果

Blends of Conjugated and Adhesive Polymers for Sticky Organic Thin‐Film Transistors

• DOI: 10.1002/aelm.202300422
• 发表时间: 2023-09
• 期刊: Advanced Electronic Materials
• 影响因子: 6.2
• 作者: James G. Sutjianto;Sang H. Yoo;Clayton R. Westerman;Thomas N. Jackson;Jonathan J. Wilker;Enrique D. Gomez

Using osmotic pressure simulations to test potentials for ions

• DOI: 10.1039/d0sm00957a
• 发表时间: 2020-11-14
• 期刊: SOFT MATTER
• 影响因子: 3.4
• 作者: Gillespie, Colin;Milner, Scott T.
• 通讯作者: Milner, Scott T.

Morphing Simulations Reveal Architecture Effects on Polymer Miscibility

变形模拟揭示了结构对聚合物混溶性的影响

• DOI: 10.1021/acs.macromol.0c01154
• 发表时间: 2020
• 期刊: Macromolecules
• 影响因子: 5.5
• 作者: Shetty, Shreya;Adams, Milena M.;Gomez, Enrique D.;Milner, Scott T.
• 通讯作者: Milner, Scott T.

Characterization of chain alignment at buried interfaces using Mueller matrix spectroscopy

使用穆勒矩阵光谱表征掩埋界面的链排列

• DOI: 10.1557/mrc.2020.19
• 发表时间: 2020
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: Smith, Bryan H.;Xie, Renxuan;Lee, Wonho;Adhikari, Dipendra;Podraza, Nikolas J.;Gomez, Enrique D.
• 通讯作者: Gomez, Enrique D.

Predicting χ of Polymer Blends Using Atomistic Morphing Simulations

使用原子变形模拟预测聚合物共混物的 α

• DOI: 10.1021/acs.macromol.1c01550
• 发表时间: 2021
• 期刊: Macromolecules
• 影响因子: 5.5
• 作者: Shetty, Shreya;Gomez, Enrique D.;Milner, Scott T.
• 通讯作者: Milner, Scott T.