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Ultralarge Contraction Directed by Light‐Driven Unlocking of Prestored Strain Energy in Linear Liquid Crystal Polymer Fibers
Author(s) -
Pang Xinlei,
Qin Lang,
Xu Bo,
Liu Quan,
Yu Yanlei
Publication year - 2020
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202002451
Subject(s) - materials science , liquid crystal , photoisomerization , lamellar structure , azobenzene , polymer , contraction (grammar) , phase transition , artificial muscle , chemical physics , nanotechnology , composite material , optoelectronics , condensed matter physics , computer science , actuator , isomerization , medicine , biochemistry , chemistry , physics , artificial intelligence , catalysis
Anisotropic 1D contraction motion of polymeric actuating materials has drawn growing interests in fields ranging from soft robotics to biomimetic muscles. Although light‐driven liquid crystal polymers (LCPs) represent promising candidates to realize contraction (<20%) triggered remotely and spatially, there remain multitudes of challenges to develop an LCP system possessing ultralarge contraction rate. Here, a novel strategy combining shape memory effect and photochemical phase transition is presented to realize light‐driven contraction as large as 81% in a newly designed linear liquid crystal copolymer, where the eutectic mesogens of azobenzene and phenyl benzoate self‐organize into the smectic B phase. Importantly, this highly ordered structure as the switching segment firmly locks the stress‐induced strain energy, which is rapidly released by reversible trans – cis photoisomerization that destroys the lamellar liquid crystal phase, therefore leading to such ultralarge contraction. Fibers serve as light‐driven building blocks to achieve precise origami, to mimic the recovery of a “broken” spider web and to screen objects in different sizes, laying new ground for advanced applications of light‐driven LCPs from biomimetic robots to human assists.