Chen Jie, Xia Xiaolu, Yan Xiaoqian, Wang Wenjing, Yang Xiaoyi, Pang Jie, Qiu Renhui, Wu Shuyi
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China.
ACS Appl Mater Interfaces. 2023 Oct 4;15(39):46440-46448. doi: 10.1021/acsami.3c06809. Epub 2023 Sep 19.
Flexible piezoresistive sensors are core components of many wearable devices to detect deformation and motion. However, it is still a challenge to conveniently prepare high-precision sensors using natural materials and identify similar short vibration signals. In this study, inspired by microstructures of human skins, biomass flexible piezoresistive sensors were prepared by assembling two wrinkled surfaces of konjac glucomannan and k-carrageenan composite hydrogel. The wrinkle structures were conveniently created by hardness gradient-induced surface buckling and coated with MXene sheets to capture weak pressure signals. The sensor was applied to detect various slight body movements, and a machine learning method was used to enhance the identification of similar and short throat vibration signals. The results showed that the sensor exhibited a high sensitivity of 5.1 kPa under low pressure (50 Pa), a fast response time (104 ms), and high stability over 100 cycles. The XGBoost machine learning model accurately distinguished short voice vibrations similar to those of individual English letters. Moreover, experiments and numerical simulations were carried out to reveal the mechanism of the wrinkle structure preparation and the excellent sensing performance. This biomass sensor preparation and the machine learning method will promote the optimization and application of wearable devices.
柔性压阻式传感器是许多可穿戴设备中用于检测变形和运动的核心部件。然而，使用天然材料方便地制备高精度传感器并识别类似的短振动信号仍然是一项挑战。在本研究中，受人类皮肤微观结构的启发，通过组装魔芋葡甘露聚糖和κ-卡拉胶复合水凝胶的两个褶皱表面制备了生物质柔性压阻式传感器。通过硬度梯度诱导的表面屈曲方便地形成褶皱结构，并涂覆MXene片以捕获微弱的压力信号。该传感器被应用于检测各种轻微的身体运动，并使用机器学习方法来增强对类似的短喉部振动信号的识别。结果表明，该传感器在低压（50 Pa）下表现出5.1 kPa的高灵敏度、快速响应时间（104 ms）以及超过100次循环的高稳定性。XGBoost机器学习模型能够准确区分与单个英文字母类似的短语音振动。此外，还进行了实验和数值模拟以揭示褶皱结构制备的机制和优异的传感性能。这种生物质传感器的制备方法和机器学习方法将推动可穿戴设备的优化和应用。
