Oriented Alginate-Poly(vinyl alcohol) Electrospun Nanofibers for Multimodal Sensing and Gesture Language Recognition

乙烯醇 材料科学 纳米纤维 可穿戴计算机 静电纺丝 耐久性 可穿戴技术 复合材料 纳米技术 计算机科学 聚合物 嵌入式系统
作者
Yu Fu,Chen Yang,Ye Tian,Boqiang Zhang,Zhenshuai Wan,Kun Zhang,Shuangkun Wang,Guoxing Jiang,Wei Liu,Ronghan Wei
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:16 (44): 61381-61394 被引量:6
标识
DOI:10.1021/acsami.4c16421
摘要

Flexible nanofiber sensors have gained substantial attention in extending application scenarios owing to their desirable lightweight, comfort, and breathability. Nevertheless, disorder and uneven dimension issues of nanofibers are the leading concerns in their multifunctional response, which often leads to erratic response signals as well as poor linearity. In this work, a high-performance oriented nanofiber film with a three-dimensional network consisting of alginate sodium, poly(vinyl alcohol), and poly(ethylene oxide) was successfully fabricated by a controllable directional electrospinning technique. The main properties of the nanofibers are capable of being regulated intentionally by varying the electrospinning temperature, collector rotation speed, and polymer concentrations. Based on the favorable structure orientation, the nanofiber film displays satisfied biodegradability and high mechanical strength (575.1 MPa). Being integrated with modified magnetic particles, the sensors not only display a fast response speed, high magnetic sensitivity, and exceptional recoverability in response to magnetic fields but also show favorable sensitivities and reliable long-term durability under mechanical excitations. As a wearable sensor, it can accurately perceive the physiological signals generated by the human body in real-time. Furthermore, with the assistance of a convolutional neural network model, a gesture language recognition system is developed by integrating multiple sensors to realize a high recognition accuracy (∼99.08%). This study provides a feasible strategy to manufacture high-performance multimodal sensors for wearable human-machine interaction applications.
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