Digital light processing of liquid crystal elastomers for self-sensing artificial muscles
ORAL
Abstract
Artificial muscles based on stimuli-responsive polymers usually exhibit mechanical compliance, versatility, and high power-to-weight ratio, showing great promise to potentially replace conventional rigid motors for next-generation soft robots, wearable electronics, and biomedical devices. In particular, thermomechanical liquid crystal elastomers (LCEs) constitute artificial muscle-like actuators that can be remotely triggered for large stroke, fast response, and highly repeatable actuations. Here, we introduce a digital light processing (DLP)–based additive manufacturing approach that automatically shear aligns mesogenic oligomers, layer-by-layer, to achieve high orientational order in the photocrosslinked structures; this ordering yields high specific work capacity (63 J kg−1) and energy density (0.18 MJ m−3). We demonstrate actuators composed of these DLP printed LCEs’ applications in soft robotics, such as reversible grasping, untethered crawling, and weightlifting. Furthermore, we present an LCE self-sensing system that exploits thermally induced optical transition as an intrinsic option toward feedback control.
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Publication: S. Li, H. Bai, Z. Liu, X. Zhang, C. Huang, L. W. Wiesner, M. Silberstein, R. F. Shepherd, Digital Light Processing of Liquid Crystal Elastomers for Self-Sensing Artificial Muscles. Science Advances 7 (30), eabg3677, 2021.
Presenters
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Shuo Li
Cornell University
Authors
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Shuo Li
Cornell University
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Hedan Bai
Cornell University
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Robert F Shepherd
Cornell University