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

纳米材料存在于许多消费品中，作为聚合物基质中的颗粒增强材料；这种材料体系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二氧化硅颗粒来提高燃油经济性。材料组合的选择通常是通过试错试验来确定的。这项研究拨款将通过使用计算建模和实验的知情方法来设计聚合物纳米复合材料，从而产生一种简单的工具来预测作为材料类型和加工条件函数的兼容性。聚合物纳米复合材料的潜在应用包括太阳能电池、体育器材、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项目吸引几名女本科生，以及通过代顿大学夏季荣誉学院和少数民族工程与技术丰富营的高中生参加。与埃塞俄比亚大学的长期关系将通过NSF加强研究参与伙伴关系(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，但在理解这种体系中如何形成层次结构方面存在着很大的差距。这项研究测试了这样一种假设，即可以准确地确定控制填料在聚合物基质中分散的参数，并在工具箱中使用该参数来预测优化的结构和性能。该方法耦合了二元混合物的准热力学分析，得到了与粗粒势有关的准二阶维里系数，该系数量化了二元热焓相互作用。该参数将用于中尺度模拟，以预测最优组成、加工条件、复杂混合物中组分的分散和/或分离、填料的关联函数和关联长度。这些特征也可以在不同的X射线和中子散射测量中通过实验确定。因此，研究工作涉及三个新的组成部分：1)使用散射计算伪二阶维里系数并确定用于模拟的势函数；2)使用第一部分中的势对二元、三元和四元混合物进行耗散粒子动力学模拟；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。我们方法的结果是基于相互验证的实验和模拟方法的复合问题的实际解决方案。

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.