Highly Sensitive and Selective Multidirectional Flexible Strain Sensors With Cross-Shaped Structure Based on Fe NWs/Graphene/Interlock Knit Fabric for Human Activity Monitoring

石墨烯 联锁 灵敏度(控制系统) 材料科学 拉伤 机械工程 计算机科学 纳米技术 电子工程 工程类 医学 内科学
作者
Pingan Yang,Xu Chen,Rui Li,Mengjie Shou,Xinglong Gong,Chul-Hee Lee,Kaibi Zhang
出处
期刊:IEEE Sensors Journal [Institute of Electrical and Electronics Engineers]
卷期号:23 (19): 23440-23447
标识
DOI:10.1109/jsen.2023.3308715
摘要

Multidirectional flexible strain sensors are critical in areas such as medicine, sports, robotics, and virtual reality. However, the current problem is that it is difficult to achieve accurate multidirectional detection while having excellent sensing performance. In this article, the sensitive layer is successfully fabricated using Fe nanowires (NWs), graphene, and interlock knit fabrics (IKFs). The cross-shaped structure multidirectional flexible strain sensor is fabricated based on the Fe NWs/graphene/IKF sensitive layer. The unique multidirectional sensing property of the cross-shaped structure designed by the principle of orthogonal configuration enables the sensor to realize multidirectional sensing, there is a clear manifestation of the direction of the applied force, but there is almost no response to the direction perpendicular to the applied force. Excellent anisotropic sensing ability within the working range of 0%–75%, with a sensitivity of 8.888 in the stretching direction and a sensitivity of 0 in the vertical stretching direction. This means that sensors can accurately identify and analyze strain changes in specific directions, thereby better supporting the multidirectional perception requirements in practical application scenarios. Moreover, the sensor has a good linear response ( ${R}^{{2}}$ = 99.0%) in a large strain range (75%–100%) and good reproducibility up to 80% strain ( $\sim $ 2000 cycles). In addition, by detecting the complex multifreedom neck and wrist movements of the human body and applying different angles of stress, multidirectional motion monitoring is realized, which shows a broad application prospect in the fields of motion monitoring and human–computer interaction.
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