GOALI: Shape Memory Polymer Composites

技术：通过研究既具有形状记忆行为又能改善物理性能的新复合材料系统，建立了一个大学和行业的合作关系，以解决有关当前基于聚合物的形状记忆技术的局限性的突出问题。在这里，形状记忆是这样的过程，通过该过程，已经变形并临时固定在该变形形状中的材料可以被触发以恢复到其原始的、未变形的、永久的形状。这种现象可用于驱动和设备部署。对于高性能应用，需要高导热性和导电性、高刚性和出色的耐用性。在这个合作研究项目中，采用了一种综合的方法来满足这些需求。独特的材料设计、加工和热机械表征(SU)与详细的微观结构和界面表征(GM)相结合，将材料成分、增强体/基质的物理和化学相互作用与复合材料的整体性能和形状记忆性能联系起来，从而实现对材料组成和力学性能的定量和力学理解。将制定设计指南，以开发具有更高性能和性能的新型形状记忆聚合物复合材料(SMPC)，为结构应用提供新的解决方案。通过比较由相同的母体组成但不同的增强体类型组成的多个材料体系，将确定增强体/特定于母体的相互作用对复合材料整体性能的影响。材料提供了极大地简化机械设计的潜力，这些设计用于制造、机械设备、机器人和包装等不同领域。这是可能的，因为与传统电机不同，结构材料本身在驱动过程中会改变形状。这一研究项目的重点是一类特别令人兴奋的智能材料，即形状记忆聚合物，SMPS。尽管开关电源前景看好，但与上述苛刻应用的要求相比，它们的机械和驱动性能到目前为止一直受到限制。在这项研究中，产学研团队将使用各种加固策略显著提高现有和可扩展的开关电源的性能。研究活动和成果将广泛影响材料科学和工程领域的研究人员，揭示新的加工和设计工具以及对复合材料性能的基本理解，在此适用于形状记忆，但广泛适用于其他性能和应用。通过整合拟议的研究和教育，研究生和本科生将有机会在快节奏的工业背景下参与研究活动，研究一个具有重大产业相关性和高创新潜力的问题。此外，拟议研究的结果将由国际翻译公司在课堂上翻译。特别是，形状记忆聚合物的设计工具将由本科生团队开发，并应用于几个现实世界的应用，包括医疗设备、家居用品和机械机械。同时，由此产生的设计工具将为整个材料领域提供一个急需的数据库，其中包含SMPC的性能、设计和形状记忆性能系数之间的相互关系。

TECHNICAL:A university-industry collaboration is established to address outstanding questions concerning the limitations of current polymer-based shape memory technology by studying new composite systems capable of both shape memory behavior and improved physical properties. Here, shape memory is the process whereby a material that has been deformed and temporarily fixed in this deformed shape can be triggered to recover to its original, un-deformed, permanent shape. Such a phenomenon can be exploited for actuation and device deployment. For high performance applications, high thermal and electrical conductivity, high stiffness, and outstanding durability are needed. In this collaborative research project, a composite approach is adopted to meet these needs. Unique materials design, processing and thermo-mechanical characterization (SU) is combined with detailed microstructural and interface characterization (GM) that will lead to quantitative and mechanistic understanding that connects material composition, and reinforcement/matrix physical and chemical interactions with the composites bulk properties and their shape memory performances. Design guidelines will be established for developing new shape memory polymer composites (SMPCs) with improved properties and performances that will offer new solutions for structural applications. By comparing multiple materials systems consisting of the same matrix composition but with varying reinforcement types, the effects of reinforcement/matrix-specific interactions on the composites bulk properties will be determined.NON-TECHNICAL:?Smart? materials offer the potential to greatly simplify mechanical designs utilized in diverse fields of manufacturing, mechanical devices, robotics, and packaging to name a few. This is possible because unlike conventional motors, the material of construction, itself, changes shape during actuation. The focus of this research project is on a particularly exciting class of smart materials, namely shape memory polymers, SMPs. Despite the promise of SMPs, their mechanical and actuation properties have thus far been limited relative to the requirements of demanding applications mentioned above. In this research, the university-industry team will significantly increase the properties of existing and scalable SMPs using a variety of reinforcement strategies. The research activities and outcomes will broadly impact researchers in the fields of materials science and engineering by revealing new processing and design tools and fundamental understanding of composite properties, herein applied to shape memory but broadly applicable to other properties and applications. By integrating the proposed research and education, graduate and undergraduate students will have the opportunity to participate in research activities within the fast-paced industrial setting on a problem with significant industrial relevance and high impact potential for innovation. In addition, the findings resulting from the proposed research will be translated in the classroom by the PI. In particular, design tools for shape memory polymers will be developed by undergraduate student teams and applied to several real world applications, including medical devices, household goods, and mechanical mechanisms. Simultaneously, the resulting design tools will provide the materials field at large with a much-needed database of the inter-relationships between properties, design, and shape memory figures-of-merit for SMPCs.