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Photo-induced spatial gradient network for shape memory polymer with pattern-memorizing surface

delete2022-01-01
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PRE
AI
T
Tiantian Li
S
Shuzhen Yan
X
Xiaxin Gao
S
Shuai Zhou
J
Jin Li
马
马小东 (Xiao-Dong Ma)
J
Jie Yin
姜
姜学松 (Xuesong Jiang) *
DOI:10.1039/d2mh00943adelete
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摘要

摘要

En 中文
Incorporating a pattern-memorizing surface into a multi-functional shape memory polymer (SMP) offers various extraordinary opportunities for their engineering applications. However, current memory-patterned approaches prepared by artificial loading are at the cost of initial balance, whose potential is greatly limited by the internal relationship between thermodynamic equilibrium and the entropy-driven pattern-memorizing cycle. Here, a robust yet effective strategy is presented for fabricating a spontaneous pattern on a poly(styrene-block-butadiene-block-styrene) (SBS)-based SMP with a gradient crosslinking network via molecular diffusion for equilibrium. Benefiting from the photo-induced diffusion of maleimide, the resulting steady-state pattern as a permanent shape ensures the recovery of morphology, and the gradient network formed by the diffusion-regulated spatial Diels-Alder (D-A) crosslinking reaction makes the pattern memory cycle from existence to elimination possible. Furthermore, taking advantage of an uneven structural network, the shape reconfigurations from 2D patterned sheets to 3D configurations with a patterned surface can be achieved conveniently through a shape memory effect, simplifying programming setups. In addition, this type of 3D shape also can shift back to a 2D patterned film via an inverse D-A decrosslinking reaction upon thermal treatment. This straightforward approach for fabricating a pattern of a single layer on an SMP surface with a spatial gradient network opens a new avenue for functional smart materials, which expands the technological perspectives in many fields of flexible electronics, smart actuators, switching sensors and soft robotics.
Keyword:
RECOVERY
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期刊

Materials Horizons 封面图
Materials Horizons
IF:
10.7
论文数:
3.6K
被引数:
2.4W

机构

S
shanghai jiao tong university
学者数:
15.7W
论文数: 11.7W
被引数: 159
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