Thermoresponsive Lignin-Reinforced Poly(Ionic Liquid) Hydrogel Wireless Strain Sensor
Author(s) -
Xinyu Qu,
Ye Zhao,
Ziang Chen,
Siying Wang,
Yanfang Ren,
Qian Wang,
Jinjun Shao,
Wenjun Wang,
Xiaochen Dong
Publication year - 2021
Publication title -
research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.8
H-Index - 16
ISSN - 2639-5274
DOI - 10.34133/2021/9845482
Subject(s) - materials science , ionic liquid , gauge factor , self healing hydrogels , toughness , strain gauge , polymer , thermal stability , smart polymer , composite material , chemical engineering , polymer chemistry , chemistry , medicine , biochemistry , alternative medicine , pathology , fabrication , engineering , catalysis
To meet critical requirements on flexible electronic devices, multifunctionalized flexible sensors with excellent electromechanical performance and temperature perception are required. Herein, lignin-reinforced thermoresponsive poly(ionic liquid) hydrogel is prepared through an ultrasound-assisted synthesized method. Benefitting from the electrostatic interaction between lignin and ionic liquid, the hydrogel displays high stretchability (over 1425%), excellent toughness (over 132 kPa), and impressive stress loading-unloading cyclic stability. The hydrogel strain sensor presents excellent electromechanical performance with a high gauge factor (1.37) and rapid response rate (198 ms), which lays the foundation for human body movement detection and smart input. Moreover, owing to the thermal-sensitive feature of poly(ionic liquid), the as-prepared hydrogel displays remarkable thermal response sensitivity (0.217°C -1 ) in body temperature range and low limit of detection, which can be applied as a body shell temperature indicator. Particularly, the hydrogel can detect dual stimuli of strain and temperature and identify each signal individually, showing the specific application in human-machine interaction and artificial intelligence. By integrating the hydrogel strain sensor into a wireless sensation system, remote motion capture and gesture identification is realized in real-time.
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