离子液体修饰三维纤维网电容式压力传感器的制备与性能

Fabrication and Performance of Ionic Liquid-Modified 3 D Fibrous Network-Based Capacitive Pressure Sensor

  • 摘要: 柔性电容式压力传感器在智能穿戴等领域具有重要应用价值,但其性能仍受限于界面电学特性与结构设计。本文通过针刺-热熔工艺构建三维纤维网介电层基材,利用低温等离子体对其进行表面改性,并在其表面修饰离子液体,同时以铜箔作为电极,设计了一种基于离子修饰三维纤维网的电容式压力传感器。通过扫描电子显微镜(SEM)、能量色散谱仪(EDS)、傅里叶变换红外光谱仪(FTIR)、物理机械性能、电学性能以及人体应用实验,对该传感器进行了全面的性能评估。实验结果表明:该传感器具有高灵敏度1.83 kPa −1,相比未经等离子体处理的电容传感器B2高出5.4倍,且比未修饰离子液体的传感器B3高出20.3倍;同时,具有快速响应/恢复速度(分别为170 ms与110 ms),宽检测范围(0-255.92 kPa),以及优异的循环稳定性。在实际应用中,该传感器能够有效检测人体吞咽、按压、抓握等细微动作信号,展示出在健康监测、人机交互等领域的广阔应用前景。

     

    Abstract: Flexible capacitive pressure sensors are highly promising for applications in smart wearable devices, yet their performance remains constrained by interfacial electrical properties and structural design limitations. In this study, we developed a three-dimensional fibrous network dielectric substrate via a needle-punching and thermal bonding process. The substrate underwent surface modification through low-temperature plasma treatment and was subsequently functionalized with ionic liquids. Utilizing copper foil as the electrode, we designed a capacitive pressure sensor based on the ion-modified three-dimensional fibrous network structure.The performance of the sensor was comprehensively evaluated through scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), fourier transform infrared spectrometer (FTIR), mechanical and physical property testing, electrical performance assessment, and human-body application experiments. The experimental results demonstrate that the sensor exhibits a high sensitivity of 1.83 kPa −1, which is 5.4 times greater than that of capacitive sensors without plasma treatment, and 20.3 times higher than that of sensors without ionic liquid modification. Additionally, the sensor possesses rapid response and recovery times (170 ms and 110 ms, respectively), a wide detection range (0–255.92 kPa), and excellent cyclic stability. In practical applications, the sensor effectively detects subtle human motion signals, such as swallowing, pressing, and grasping, showcasing its significant potential for applications in health monitoring, human-machine interfaces, and related fields.

     

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