Collaborative Research: Processing Films from Multi-Functional Polymer Dispersion Blends

合作研究：用多功能聚合物分散体共混物加工薄膜

• 批准号: 1727603
• 负责人: Yaroslava Yingling
• 金额: $ 28.55万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727603&HistoricalAwards=false
• 关键词: Collaborative; Research; Processing; Films; Multi

Polymer dispersions are a general class of materials in which fine particles of polymers are dispersed in water. These materials are used commonly in several industries, including medicine, paints and coatings, automobiles and plastics, and equipment exists for processing these traditional polymer dispersions. However, processing thin films from multi-functional polymer dispersion blends (combining two or more types of dispersed particles) poses a significant challenge due to lack of fundamental understanding. Control over film properties is extremely difficult because minor changes in polymer dispersion processing conditions or chemistry can lead to unpredictable particle sizes and shapes. This award will enable a path towards the rational design of polymer dispersion blends to achieve films with multifunctional properties, such as films with responsive optical properties, reusable anti-microbial films, and mechanically-robust organic solar cells. The new knowledge generated by this award has the potential for rapid impact on existing industrial applications by helping them become more sustainable, as well as the development of new applications. This award will provide research opportunities to underrepresented groups in science and engineering and help disseminate science and engineering knowledge to the public.The overall goal of this research is to establish fundamental process-structure-property relations to guide the predictive design of blended polymer dispersions for multi-functional applications. This work will use a systematic, synergistic and iterative approach among simulations, synthesis and characterization to understand the morphology and self-assembly of multiple components with variable miscibility in a solvent, such as emulsions comprising two or more organic macromolecules, primary organic solvent, secondary co-solvent, surfactant, and water. Polymer dispersions will be simulated by a mesoscale computational method that has been used to study and predict the phase behavior and properties of emulsification, polymer aggregates, and complex self-assembled structures. Emulsion-based, resonant infrared matrix-assisted pulsed laser evaporation will be used for the thin-film deposition of polymer dispersions, which is critical to eliminate the mutual dependence between the emulsion process and the nature of film formation. This deposition process, specifically the laser-target interaction and laser-generated plume characteristics, will be explained using coarse-grained molecular dynamics. The deposited films will be investigated using standard materials and device characterization techniques. The outcomes of this work will provide a predictive theoretical framework to elucidate the mechanisms that determine how disparate polymer dispersions blend in solution and in films.

聚合物分散体是一类一般的材料，其中的聚合物微粒分散在水中。这些材料通常用于几个行业，包括医药、油漆和涂料、汽车和塑料，并且存在用于加工这些传统聚合物分散体的设备。然而，由于缺乏基本的理解，从多功能聚合物分散共混物（结合两种或两种以上类型的分散颗粒）加工薄膜构成了重大挑战。由于聚合物分散处理条件或化学性质的微小变化可能导致不可预测的颗粒大小和形状，因此控制薄膜性能非常困难。该奖项将为聚合物分散混合物的合理设计开辟一条道路，以实现具有多功能特性的薄膜，例如具有响应光学特性的薄膜，可重复使用的抗菌薄膜和机械坚固的有机太阳能电池。该奖项产生的新知识有可能对现有的工业应用产生快速影响，帮助它们变得更加可持续，并开发新的应用。该奖项将为科学和工程领域代表性不足的群体提供研究机会，并帮助向公众传播科学和工程知识。本研究的总体目标是建立基本的工艺-结构-性能关系，以指导多功能应用的共混聚合物分散体的预测设计。这项工作将在模拟、合成和表征之间使用系统的、协同的和迭代的方法来理解在溶剂中具有可变混溶性的多种组分的形态和自组装，例如由两种或更多有机大分子、初级有机溶剂、次级助溶剂、表面活性剂和水组成的乳液。聚合物分散体将通过中尺度计算方法进行模拟，该方法已用于研究和预测乳化、聚合物聚集体和复杂自组装结构的相行为和性质。基于乳化的共振红外基质辅助脉冲激光蒸发将用于聚合物分散体的薄膜沉积，这对于消除乳化过程与成膜性质之间的相互依赖性至关重要。这个沉积过程，特别是激光-目标相互作用和激光产生的羽流特性，将使用粗粒度分子动力学来解释。沉积薄膜将使用标准材料和器件表征技术进行研究。这项工作的结果将提供一个预测的理论框架，以阐明决定不同聚合物分散体如何在溶液和薄膜中混合的机制。

项目成果

Soft Matter Informatics: Current Progress and Challenges

• DOI: 10.1002/adts.201800129
• 发表时间: 2018-11
• 期刊: Advanced Theory and Simulations
• 影响因子: 3.3
• 作者: James S. Peerless;Nina J. B. Milliken;Thomas J. Oweida;Matthew D. Manning;Yaroslava G. Yingling