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：建议的计算指导方法将提供加速的材料设计框架，对学术和工业加速柔性电子设备用共轭聚合物的开发都是有用的。这项工作旨在利用共轭聚合物基本性质的预测和测量方面的进展，并沿着材料开发连续体向前推进。通过开发新的工具和改进微观结构如何控制共轭聚合物中的电荷传输的概念，主要努力整合了理论、模拟和实验。此外，首席调查员将制定一项雄心勃勃的外展试点计划，将研究活动作为改善非博士机构(如社区大学)学生的教育机会和结果的工具。宾夕法尼亚州立大学是更广泛的高等教育生态系统的一个独特缩影，因为它由一个中心研究密集型校园与19个主要为两年制英联邦校区整合在一起，为更多样化的学生群体提供服务--使宾夕法尼亚州立大学成为探索将研究作为招生和留住工具的理想孵化器。与宾夕法尼亚州立大学莱昂哈德加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基于网络的研究体验，目标是宾夕法尼亚州立大学英联邦校园的科学、技术、工程和数学(STEM)学生。技术描述：这项提议中的工作利用先前的进展来预测持续长度、玻璃化转变温度和向列相到各向同性转变温度。拟议的项目旨在通过开发能够加快机械和导电性能预测的工具，进一步推进计算材料设计。将开发三种计算工具：基于力匹配的粗粒度模型用于加速液晶半柔性聚合物的计算设计，链收缩模拟用于预测液晶结晶度对纠缠的影响，以及紧束缚模型用于探索堆积和无序对电荷传输的作用。模拟和实验的结合将是产生准确的粗粒度模拟的关键，该模拟能够通过将分子动力学模拟与自洽场理论计算相结合的主要研究人员的方法来预测液晶度。这将使对主链和侧链结构的系统计算探索成为可能，这些结构通过选定的合成模型材料进行验证。模拟和实验对于将向列相有序纳入首席研究人员的聚合物纠缠统一理论也将是至关重要的，从而提供一种能够从化学结构预测共轭聚合物的流变性(例如机械性能)的工具。此外，紧束缚模型将预测堆积和局域无序在电荷传输中的作用，以探索层状无序相通过促进pi堆积在促进有效的电荷传输中发挥关键作用的假设。这些模型将通过测量电荷迁移率作为温度的函数以及在不同的晶体、液晶或各向同性相内进行验证。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.