基于1D/2D层级杂化网络的CS-MXene-NH2-CNTs/PU海绵压力传感器的制备及性能

Preparation and electromechanical performance of CS-MXene-NH2-CNTs/PU composite sponge pressure sensor based on 1D/2D hierarchical hybrid networks

  • 摘要: 针对目前柔性压力传感器在灵敏度与检测范围之间难以兼顾的缺点,本文以聚氨酯(PU)海绵为基底材料,通过静电逐层自组装技术,利用带正电的壳聚糖(CS)、氨基化碳纳米管(NH2-CNTs)与带负电的MXene之间的静电相互作用,构筑了具有层级导电网络的CS-MXene-NH2-CNTs/PU(CMNP)海绵柔性压力传感器。表征了CMNP海绵的表面形貌,研究了传感器的传感机制、力学性能和机电响应特性。结果表明,得益于一维(1D)NH2-CNTs与二维(2D)MXene形成的杂化导电网络以及微裂纹效应与接触电导协同机制,该传感器具有34.93 kPa−1的高灵敏度(1.83~4.98 kPa),极低的检测限(100 Pa)、超宽的检测范围(1000 kPa)、快速响应/恢复时间(40 ms/46 ms)、以及优异的循环稳定性(> 12000次)。该传感器可应用于人体健康监测与运动识别领域,并结合一维卷积神经网络(1D-CNN)构建了智能触觉交互键盘系统,实现了对5种典型动态触觉动作100%的整体识别准确率,在可穿戴设备和人机交互等领域具有较好的应用前景。

     

    Abstract: To address the shortcoming that current flexible pressure sensor struggle to balance sensitivity and detection range, a chitosan-MXene-amino-functionalized carbon nanotubes/polyurethane (CS-MXene-NH2-CNTs/PU, denoted as CMNP) composite sponge flexible pressure sensor with a hierarchical conductive network was constructed using polyurethane (PU) sponge as the substrate through an electrostatic layer-by-layer self-assembly technique. The electrostatic interaction between the positively charged chitosan (CS), amino-functionalized carbon nanotubes (NH2-CNTs), and negatively charged MXene was utilized. The surface morphology of the CMNP sponge was characterized, and the sensing mechanism, mechanical properties, and electromechanical response characteristics of the sensor were investigated.The results show that, benefiting from the hybrid conductive network formed by one-dimensional (1D) NH2-CNTs and two-dimensional (2D) MXene as well as the synergistic mechanism of micro-crack effect and contact conductance, the sensor possesses a high sensitivity of 34.93 kPa−1 (1.83~4.98 kPa), a very low detection limit (100 Pa), a wide detection range (1000 kPa), fast response/recovery times 40 ms/46 ms), and excellent cyclic stability (> 12000 cycles). The developed sensor holds great promise for human health monitoring and motion recognition applications. Furthermore, by integrating this sensor with a one-dimensional convolutional neural network (1D-CNN), an intelligent tactile interactive keyboard system was constructed, achieving a 100% overall accuracy in identifying five typical dynamic tactile actions. This work highlights the broad application prospects of the sensor in wearable electronics and human-machine interaction.

     

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