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Tuning the reversibility of hair artificial muscles by disulfide cross-linking for sensors, switches, and soft robotics

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Abstract

Tensile and torsional artificial muscles from biocompatible and biodegradable materials are highly desired for soft robotics, sensors, and controllers in bio-related applications. Twisted fibers can be used to prepare tensile and torsional artificial muscles, while torsional tethering is always required to avoid release of the inserted twist, which adds complexity to the device design. Moreover, the tuning of the reversibility of twisted fiber artificial muscles has not been realized. Here disulfide cross-linking was used to prepare novel tether-free hygroresponsive tensile and torsional fiber artificial muscles in twisted hair fibers. Increasing the cross-linking level converted the fiber artificial muscle from irreversible to reversible actuation. Different types of actuations including rotation, contraction, and elongation were realized for the twisted, the homochirally coiled, and the heterochirally coiled hair fibers, respectively. A reversible torsional fiber artificial muscle showed 122.4° mm−1 rotation, homochiral and heterochiral fiber artificial muscles showed 94% contraction and 3000% elongation, respectively, and a maximum work capacity and energy density of 6.35 J kg−1 and 69.8 kJ m−3, respectively, were realized, on exposure to water fog. This work provides a new strategy for preserving the inserted twist in bio-fiber artificial muscles and for tuning of muscle reversibility, which show application perspectives in biocompatible smart materials, sensors, and robotics.

Graphical abstract: Tuning the reversibility of hair artificial muscles by disulfide cross-linking for sensors, switches, and soft robotics

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Article information


Submitted
06 Feb 2021
Accepted
11 Mar 2021
First published
12 Mar 2021

Mater. Horiz., 2021, Advance Article
Article type
Communication

Tuning the reversibility of hair artificial muscles by disulfide cross-linking for sensors, switches, and soft robotics

X. Leng, X. Zhou, J. Liu, Y. Xiao, J. Sun, Y. Li and Z. Liu, Mater. Horiz., 2021, Advance Article , DOI: 10.1039/D1MH00234A

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