Towards Multiscale Modeling and Optimization in Design of Polymer Blend Nanocomposites

化工过程行业的市场竞争力与产品差异化直接相关。因此，大量的研究工作一直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，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最终与其微观结构相关的特定最终用途特性。目前的产品开发策略通常基于试错实验或过去的经验，不足以满足快速引入新的上级材料的需要。这是非常重要的，以开发新的预测计算方案，以评估聚合物和纳米粒子候选人的纳米复合材料的设计感兴趣的应用。这些计算方法应该允许快速和准确地确定大范围的纳米颗粒和聚合物材料的关键物理和机械性能以及它们的相互作用。此后，可以在有限数量的有希望的候选材料上进行实验。本研究提出了一种新的策略，通过理论和实验相结合的研究，以确定聚合物共混物纳米复合材料的最佳设计。以下交织的研究重点是解决：（i）预测聚合物和纳米粒子的物理和机械性能的基础上，他们的分子结构;（ii）新的实验方法，使可视化的纳米粒子的空间分布在聚合物共混物在剪切，以帮助模型开发以及验证建议的设计;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优化框架。所提出的方法有可能显着推进和改造的方法，发现新的聚合物纳米复合材料，通过解决所需的性能和工艺性能并行。商业应用包括开发导电聚合物共混物，用于能源系统和水和废水处理膜。

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.