Collaborative Research: Processing and Performance of Chained Magnetic Particle Composites for Soft Robotics

软机器人是一个快速发展的领域，其中软材料，如聚合物，形成的设备，其机械反应可以由物理或化学刺激触发。含有嵌入磁性颗粒的磁响应聚合物复合材料对软机器人特别有吸引力，因为它们允许使用电磁铁或永磁体进行非接触驱动。此外，将磁性颗粒链接在聚合物内，增强了定向响应。该奖项支持用于软机器人的链状磁性颗粒复合材料的加工和性能的基础实验和理论研究。本研究开发的基于链式磁颗粒复合材料的软机器人设备将立即在实际应用中发挥作用，包括用于下一代医疗诊断设备的非接触式蠕动泵和远程驱动的微流体阀和混合器。本研究中开发的理论工具也将通过提供易于获取的指标来推动创新，为链式磁颗粒执行器的设计和评估性能提供信息。这项研究的主题和结果将纳入旨在激励和招募下一代科学家和工程师的教育和推广活动。虽然已经有许多关于软机器人的磁活性弹性体的报道，但这些材料的设计通常是经验的，而不是由预测模型指导的，导致错过了优化其设计和性能的机会，例如链状颗粒配方。本研究旨在开发一个框架和价值的数字，以预测磁活性弹性体执行器的行为，并利用它们来设计具有增强功能的有用设备。利用这些工具，链粒磁活性弹性体在外加磁场中产生的扭矩将最大化，聚合物的弹性和弹性不稳定性将被纳入器件功能，形状记忆聚合物将实现可重构和可选择性寻址的磁活性弹性体材料和器件。这项研究将通过开发工具来指导链粒子磁活性弹性体在软机器人中的应用设计，并通过展示这些材料和设备的能力，推动链粒子磁活性弹性体领域的发展。

Soft robotics is a rapidly growing field in which soft materials, such as polymers, are formed into devices whose mechanical response can be triggered by physical or chemical stimuli. Magnetically responsive polymer composites containing embedded magnetic particles are especially attractive for soft robotics, because they allow for non-contact actuation with electromagnets or permanent magnets. Furthermore, chaining the magnetic particles within the polymer imparts an enhanced, directional response. This award supports fundamental experimental and theoretical research to investigate the processing and performance of chained magnetic particle composites for soft robotics. Soft robotic devices based on chained magnetic particle composites developed in this research will be immediately useful in practical applications, including non-contact peristaltic pumps and remotely actuated microfluidic valves and mixers for next-generation medical diagnostic devices. Theoretical tools developed in this research will also drive innovation by providing easily accessible metrics to inform the design and evaluate the performance of chained magnetic particle actuators. Themes and results from this research will be integrated into education and outreach activities designed to inspire and recruit the next generation of scientists and engineers.While there have been many reports of magnetoactive elastomers for soft robotics, the design of these materials is usually empirical rather than guided by a predictive model, resulting in missed opportunities to optimize their design and performance, such as with chained-particle formulations. This research aims to develop a framework and figures of merit for predicting the behavior of magnetoactive elastomer actuators and to employ them for designing useful devices with enhanced capabilities. Using these tools, the torque in chained-particle magnetoactive elastomers generated in applied magnetic fields will be maximized, elasticity of the polymer and elastic instabilities will be incorporated in device function, and shape memory polymers will enable realization of magnetoactive elastomer materials and devices that are reconfigurable and selectively addressable. This research will advance the field of chained-particle magnetoactive elastomers by developing tools to guide their design for applications in soft robotics and by demonstrating the capabilities of these materials and devices.