Highly Elastic, Fatigue-Resistant, and Antifreezing MXene Functionalized Organohydrogels as Flexible Pressure Sensors for Human Motion Monitoring

被引:4
|
作者
Han, Yutong [1 ]
Cao, Yuzhong [1 ]
Zhuang, Haozhe [1 ]
Yao, Yu [1 ]
Cao, Huina [1 ]
Li, Zhanhong [1 ]
Wang, Zifeng [1 ]
Zhu, Zhigang [1 ]
机构
[1] Univ Shanghai Sci & Technol, Sch Hlth Sci & Engn, Shanghai 200093, Peoples R China
基金
中国国家自然科学基金;
关键词
organohydrogels; MXene; flexible pressure sensors; deep learning algorithms; human-computer interaction;
D O I
10.1021/acsami.4c12852
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Conductive organohydrogels-based flexible pressure sensors have gained considerable attention in health monitoring, artificial skin, and human-computer interaction due to their excellent biocompatibility, wearability, and versatility. However, hydrogels' unsatisfactory mechanical and unstable electrical properties hinder their comprehensive application. Herein, an elastic, fatigue-resistant, and antifreezing poly(vinyl alcohol) (PVA)/lipoic acid (LA) organohydrogel with a double-network structure and reversible cross-linking interactions has been designed, and MXene as a conductive filler is functionalized into organohydrogel to further enhance the diverse sensing performance of flexible pressure sensors. The as-fabricated MXene-based PVA/LA organohydrogels (PLBM) exhibit stable fatigue resistance for over 450 cycles under 40% compressive strain, excellent elasticity, antifreezing properties (<-20 degrees C), and degradability. Furthermore, the pressure sensors based on the PLBM organohydrogels show a fast response time (62 ms), high sensitivity (S = 0.0402 kPa(-1)), and excellent stability (over 1000 cycles). The exceptional performance enables the sensors to monitor human movements, such as joint flexion and throat swallowing. Moreover, the sensors integrating with the one-dimensional convolutional neural networks and the long-short-term memory networks deep learning algorithms have been developed to recognize letters with a 93.75% accuracy, representing enormous potential in monitoring human motion and human-computer interaction.
引用
收藏
页码:64002 / 64011
页数:10
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