Towards Multiscale Modeling and Optimization in Design of Polymer Blend Nanocomposites

化工流程行业的市场竞争力与产品差异化直接相关。因此，大量的研究工作致力于特种化学品的开发，包括聚合物共混物和聚合物纳米复合材料。尽管付出了巨大的努力，但这些材料的设计仍然是一个重大的科学挑战。与以化学结构为特征的商品聚合物不同，聚合物共混物和聚合物纳米复合材料具有特定的最终用途属性，这些属性最终与其微观结构有关。目前的产品开发策略，通常是基于反复试验或过去的经验，不足以满足快速引入新的、优质材料的需要。开发新的预测计算方案来评估聚合物和纳米颗粒候选，对于设计感兴趣的纳米复合材料具有重要意义。这些计算方法应该能够快速、准确地确定大范围纳米颗粒和聚合物材料及其相互作用的关键物理和机械性能。此后，可以在有限数量的有希望的候选材料上进行实验。这一建议探索了一种新的策略，通过理论和实验研究相结合的方式来确定聚合物共混纳米复合材料的最佳设计。以下是相互交织的研究方向：(I)基于分子结构的聚合物和纳米粒子物理和机械性能的预测；(Ii)能够可视化纳米粒子在聚合物共混物中剪切的空间分布的新的实验方法，以帮助模型开发和建议的设计的验证；以及(Iii)通过多尺度建模同时进行材料设计和工艺设计的新的优化框架。通过同时解决所需的性能和工艺性能，所提出的方法有可能极大地推进和改变发现新型聚合物纳米复合材料的方法。商业应用包括开发导电聚合物混合物，以及用于水和废水处理的能源系统和膜。

Market competitiveness in the chemical process industry is directly related to product differentiation. As a result, significant research efforts have been devoted to the development of specialty chemicals, including polymer blends and polymer nanocomposites. Despite great efforts, the design of these materials still remains a major scientific challenge. Unlike commodity polymers, which are characterized by their chemical structure, polymer blends and polymer nanocomposites have specific end-use properties that are ultimately connected to their microstructure. Current product development strategies, typically, based on trial-and-error experimentation or past experience are not adequate to fulfill the need for rapid introduction of new, superior materials. It is of great importance to develop new predictive computational schemes to evaluate polymer and nanoparticle candidates for the design of nanocomposites for an application of interest. These computational methods should allow fast and accurate determination of key physical and mechanical properties for a large scope of nanoparticles and polymer materials as well as their interactions. Thereafter, experiments can be done on a limited number of promising candidate materials. This proposal investigates a novel strategy to identify optimal designs of polymer blend nanocomposites through a combination of theoretical and experimental research. The following intertwined research thrusts are addressed: (i) prediction of polymer and nanoparticle physical and mechanical properties based on their molecular structure; (ii) novel experimental methods to enable visualization of nanoparticles' spatial distribution in polymer blends under shear to aid model development as well as validation of the suggested designs; and (iii) novel optimization framework in simultaneous materials design and process design through modeling at multiple scales. The proposed approach has the potential to significantly advance and transform the approaches for discovery of new polymer nanocomposites by addressing the desired properties and process performance in parallel. Commercial applications include development of conductive polymer blends with applications in energy systems and membranes for water and wastewater treatment.