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.

*This work was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-18-1-0243, Office of Naval Research (ONR) under grant no. N00014-17-1-2837, and NSF under grant nos. EFMA-1830924, DMR-1719875, and CMMI-1825444. Part of the study was performed at the Cornell Energy Systems Institute, Cornell Center for Materials Research Shared Facilities, which are supported through the NSF MRSEC program (grant no. DMR-1719875), and Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the NSF (grant no. NNCI-2025233).

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

  • Shuo Li

    • Cornell University

Authors

  • Shuo Li

    • Cornell University
  • Hedan Bai

    • Cornell University
  • Robert F Shepherd

    • Cornell University