Collaborative Research: Enabling Design of Polymer Nanocomposites Guided by Mesoscale Simulations and Scattering Experiments

在许多消费品中发现纳米材料在聚合物基质中用作颗粒增强;该材料系统被称为聚合物纳米复合材料。一个这样的例子是在轮胎中使用纳米级二氧化硅颗粒来提高燃料经济性。材料组合的选择通常通过反复试验来确定。 这项研究资助将使聚合物纳米复合材料的设计通过使用计算建模和实验的知情方法，从而产生一个简单的工具来预测作为材料类型和加工条件的函数的兼容性。聚合物纳米复合材料的潜在应用包括太阳能电池、运动设备、医疗器械和航空航天结构。 该项目将通过辛辛那提大学的一个方案吸引几名女本科生参加，并通过代顿大学暑期荣誉学院和少数族裔工程技术充实营吸引高中生参加。 与埃塞俄比亚大学的长期关系将通过NSF增强研究参与合作伙伴关系（PEER）计划的赠款来利用。多组分聚合物混合物，如纳米复合材料是最常用的聚合物材料之一，但在理解分层结构如何在这样的系统中发展存在显着的差距。本研究测试的假设，它是可能的，以准确地确定一个参数控制填料在聚合物基体中的分散，并采用此参数在工具箱中预测优化的结构和性能。该方法耦合一个伪热力学分析的二元混合物，以获得一个伪二阶维里系数，它量化的二元相互作用，并涉及到一个粗粒度的潜力。该参数将用于中尺度模拟，以预测复杂共混物中组分的最佳组成、加工条件、分散和/或分离、填料的相关函数和相关长度。这些特征也可以在单独的X射线和中子散射测量中通过实验确定。因此，研究工作包括三个新的组成部分：1）表的伪二阶维里系数使用散射和确定的潜在功能的模拟; 2）耗散粒子动力学模拟的二元，三元，四元混合物使用的潜力第一部分; 3）X射线和中子散射，显微镜，动态力学和流变学测量，以验证模拟结果。我们的方法的结果是一个实际的解决方案，复合问题的基础上，相互验证的实验和模拟方法。

Nanoscale materials are found in many consumer products to serve as particle reinforcement in a polymer matrix; the material system is called a polymer nanocomposite. One such example is the use of nanoscale silica particles in tires to improve fuel economy. Choices of material combinations are often determined through trial and error experiments. This research grant will enable the design of polymer nanocomposites via an informed approach using computational modeling and experiments, resulting in a simple tool to predict compatibility as a function of material types and processing conditions. Potential applications for polymer nanocomposites include solar cells, sports equipment, medical devices and aerospace structures. The project will involve several female undergraduate students through a program at the University of Cincinnati as well as high school students through the University of Dayton Summer Honors Institute and the Minority Engineering & Technology Enrichment Camp. Long-standing relationships with Ethiopian universities will be leveraged via grants through the NSF Partnerships for Enhanced Engagement in Research (PEER) program. Multicomponent polymer mixtures such as nanocomposites are among the most commonly used polymeric materials, but there is a significant gap in the understanding of how hierarchical structure develops in such systems. This research tests the hypothesis that it is possible to accurately determine a parameter controlling filler dispersion in a polymer matrix, and to employ this parameter in a toolbox to predict optimized structure and performance. The approach couples a pseudo-thermodynamic analysis of binary mixtures to obtain a pseudo-second order virial coefficient which quantifies binary enthalpic interactions, and which relates to a coarse-grained potential. This parameter will be employed in mesoscale simulations to predict optimal compositions, processing conditions, dispersion and/or segregation of components in complex blends, correlation functions and correlation lengths for fillers. These features can also be experimentally determined in separate x-ray and neutron scattering measurements. Therefore, the researched work involves three novel components: 1) tabulation of pseudo-second order virial coefficients using scattering and determination of potential functions for simulation; 2) dissipative particle dynamics simulations of binary, ternary, quaternary mixtures using potentials from part 1; 3) x-ray and neutron scattering, microscopy, dynamic mechanical and rheological measurements to verify simulation results. The outcome of our approach is a practical solution to compounding issues, based on a mutually validating experimental and simulation methodology.