Collaborative Research: Processing and Performance of Chained Magnetic Particle Composites for Soft Robotics
Elon University, Elon NC
Investigators
Abstract
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.
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