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羊毛角蛋白 3D“分子弹簧”的介观重建用于可调谐超灵敏和高回复应变传感器。

Meso-Reconstruction of Wool Keratin 3D "Molecular Springs" for Tunable Ultra-Sensitive and Highly Recovery Strain Sensors.

机构信息

Research Institution for Biomimetics and Soft Matter, College of Materials, College of Physical Science and Technology, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, 361005, China.

Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore, 117542, Singapore.

出版信息

Small. 2020 Jun;16(24):e2000128. doi: 10.1002/smll.202000128. Epub 2020 May 11.

DOI:10.1002/smll.202000128
PMID:32390319
Abstract

Wool keratin (WK) consists of a large number of α-helices, which are just like many molecular-scale springs. Herein, the construction of 3D WK molecular spring networks are reported by cross-linking individual WK molecules via a Michael addition reaction. The as-prepared springs display a superior recovery capability with unusual nonlinear elasticity, very low dissipative energy, and turntable elastic constant achieved by adjusting the chemical crosslinking density of WK networks. Owing to these unique characteristics, the 3D WK networks based flexible strain sensors reveal a high sensitivity, broad sensing ranges, and extremely long and stable performance. While normal highly sensible strain sensors, obtained by highly sophisticated surface or bulk patterning, often exhibit a relatively narrow range of measurements and limited life cycles. Such the WK mediated sensing materials have widespread applications in wearable electronics, such as detection and tracking of different human motions, and even discern voice during speaking.

摘要

羊毛角蛋白(WK)由大量的α-螺旋组成,就像许多分子尺度的弹簧一样。在此,通过迈克尔加成反应交联单个 WK 分子来构建 3D WK 分子弹簧网络。所制备的弹簧具有优异的恢复能力,具有异常的非线性弹性、非常低的耗散能量和通过调整 WK 网络的化学交联密度获得的转盘弹性常数。由于这些独特的特性,基于 3D WK 网络的柔性应变传感器具有高灵敏度、宽感测范围以及非常长且稳定的性能。而通过高度复杂的表面或体图案化获得的正常高灵敏度应变传感器,通常表现出相对较窄的测量范围和有限的生命周期。这种 WK 介导的传感材料在可穿戴电子产品中具有广泛的应用,例如检测和跟踪不同的人体运动,甚至在说话时识别声音。

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