Tough, self-healing, adhesive double network conductive hydrogel based on gelatin-polyacrylamide covalently bridged by oxidized sodium alginate for durable wearable sensors

被引:17
|
作者
Wang, Zengsheng [1 ]
Xu, Lijian [1 ]
Liu, Weiling [2 ]
Chen, Yi [3 ]
Yang, Qiannian [1 ]
Tang, Zengmin [1 ]
Tan, Haihu [1 ]
Li, Na [1 ]
Du, Jingjing [1 ]
Yu, Maolin [1 ]
Xu, Jianxiong [1 ]
机构
[1] Hunan Univ Technol, Coll Life Sci & Chem, Hunan Key Lab Biomed Nanomat & Devices, Zhuzhou 412007, Peoples R China
[2] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412007, Peoples R China
[3] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
基金
中国国家自然科学基金; 湖南省自然科学基金;
关键词
Self-healing hydrogel; Polyaldehyde bridge; Wearable sensors; Health monitoring; Gesture recognition; ZWITTERIONIC HYDROGEL;
D O I
10.1016/j.ijbiomac.2024.133802
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Pursuing high-performance conductive hydrogels is still hot topic in development of advanced flexible wearable devices. Herein, a tough, self-healing, adhesive double network (DN) conductive hydrogel (named as OSA(Gelatin/PAM)-Ca, O-(G/P)-Ca) was prepared by bridging gelatin and polyacrylamide network with functionalized polysaccharide (oxidized sodium alginate, OSA) through Schiff base reaction. Thanks to the presence of multiple interactions (Schiff base bond, hydrogen bond, and metal coordination) within the network, the prepared hydrogel showed outstanding mechanical properties (tensile strain of 2800 % and stress of 630 kPa), high conductivity (0.72 S/m), repeatable adhesion performance and excellent self-healing ability (83.6 %/79.0 % of the original tensile strain/stress after self-healing). Moreover, the hydrogel-based sensor exhibited high strain sensitivity (GF = 3.66) and fast response time (<0.5 s), which can be used to monitor a wide range of human physiological signals. Based on this, excellent compression sensitivity (GF = 0.41 kPa(-1) in the range of 90-120 kPa), a three-dimensional (3D) array of flexible sensor was designed to monitor the intensity of pressure and spatial force distribution. In addition, a gel-based wearable sensor was accurately classified and recognized ten types of gestures, achieving an accuracy rate of >96.33 % both before and after self-healing under three machine learning models (the decision tree, SVM, and KNN). This paper provides a simple method to prepare tough and self-healing conductive hydrogel as flexible multifunctional sensor devices for versatile applications in fields such as healthcare monitoring, human-computer interaction, and artificial intelligence.
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页数:14
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