东华大学：研究基于石墨烯纤维的高性能压力传感器，具有双边致密结构，用于人体运动监测

柔性压力传感器因能精确检测外部压力并贴合复杂曲面，在健康监测与人机交互领域备受关注。然而，灵敏度、检测范围与机械稳定性之间的固有权衡严重制约了其性能提升与进一步发展。研究采用湿法纺丝与基底辅助干燥策略，通过调控石墨烯纤维（GF）的形态结构优化导电网络。最终成功制备出高导电性（3.19×10? S m??）、抗拉强度达179.6 MPa、应变能力达6.5%的致密带状GF，并将其应用于柔性压力传感器的传感层。

该基石碳纤维压力传感器在0-10kPa范围内展现出30.79kPa??的高灵敏度，即使检测范围扩展至150kPa仍保持15.59kPa??的灵敏度。此外，其响应/恢复特性迅捷（88毫秒/72毫秒），经10000次循环测试仍保持优异耐久性。该传感器能有效检测关节弯曲、腕部屈曲及脉搏跳动等细微生理信号，彰显其在健康监测与智能可穿戴设备领域的应用潜力。

2图文导读

图1.Design and applications of GF. (a) Schematic diagram of GO sheet alignment. (b) Schematic diagram of the GF-based sensing layer fabrication process. (c) Cross-sectional schematic diagram of the GF. (d) Schematic diagram of the GF pressure sensor. (e) Working mechanism of the GF pressure sensor. (f) Application of the GF sensor in motion detection.

图2.(a) Cross-sectional SEM images of GF with different routes. (b) Surface SEM images of GF with different routes. (c) Simulated snapshot before solvent evaporation during substrate-assisted drying. PE substrate is shown in gray; in the GO sheets, carbon atoms are cyan, oxygen atoms red, and hydrogen atoms white; acetic acid molecules are orange, and water molecules are purple. The initial simulation box size is 8?nm?×?8?nm?×?9.52?nm. (d) Simulated snapshot after solvent evaporation during substrate-assisted drying. (e) Changes in interaction energies between GO-GO and GO-PE interfaces. (f) Digital photograph of GF thickness measured by mechanical methods. (g) Digital photograph of GF. (h) Digital photograph of the GOF fusion-assembled sensing layer. (i) Digital photograph of the GF sensing layer. (j) Digital photograph of the GF sensor.

图3. Microstructural characterization and DFT simulation of GF prepared via different routes. (a) XRD patterns. (b) Statistical analysis of Lc. (c) Raman spectra. (d) Statistical analysis ofID/IGand La. (e) XPS spectra before and after reduction. (f) XPS C 1s spectra before and after reduction. (g) Electrical conductivity. (h) Mechanical properties. (i) Molecular models of graphene layer stacking in two extreme modes. (j)I–Vcurve of the molecular model. (k) Transmission spectra of the molecular model at zero bias. (l) Spatial distribution of the highest occupied molecular orbital (HOMO) of the molecular model.

图4.Sensing performance tests of the GF pressure sensor. (a) Current response during pressure loading and unloading. (b)I–Vcurves under different pressure loads. (c) Sensitivity curve of the GF sensor. (d) Current signal response under different pressure loads. (e) Current signal response at different compression rates. (f) Response and recovery time. (g) Long-term durability. (h) Comparison of sensitivity and detection range with other reported pressure sensors.

图5.Application of GF sensors in human motion detection. (a) Finger pressing. (b) Different pronunciations. (c) Breath detection. (d) Elbow bending. (e) Knee bending. (f) Amplified pulse signal after walking. (g) Pulse detection after walking and running. (h) Amplified pulse signal after running.

图6.Application of GF sensors in gesture recognition and Morse code. (a) Photograph of the sensor attached to a finger. (b) Schematic diagram of input and output in the signal acquisition process. (c) Sensor response to different finger bending angles. (d) Ten hand gestures representing numbers from 1 to 10 and the corresponding signals from five fingers. (e) Morse code for “DHU” and “JXU” based on finger bending. (f) Circuit diagram of the robotic hand control system. (g) Synchronized gesture control of the robotic hand.

3小结

综上所述，成功制备了基于双面高密度玻璃纤维（GF）的高性能柔性压力传感器。通过采用湿法纺丝技术结合基板辅助干燥策略，优化了GF的形态和层间堆叠结构，从而获得了高电导率（3.19×10? S m??）和优异的机械性能（抗拉强度179.6 MPa，断裂应变6.5%）。制备的GF压力传感器在0-10 kPa压力范围内展现出30.79 kPa??的超高灵敏度，当检测范围扩展至150 kPa时仍保持15.59 kPa??的高灵敏度。此外，该器件展现出快速响应/恢复特性（响应时间88毫秒/恢复时间72毫秒），并在经历10000次加载循环后仍保持稳定的传感性能。该传感器能精确捕捉人体运动信号，如指关节弯曲、喉部振动和脉搏搏动，凸显其在健康监测、智能可穿戴设备及人机交互领域的巨大潜力。本研究为柔性传感材料设计提供了新思路，为高性能压阻式传感器的开发奠定了基础。

文献：

https://doi.org/10.1016/j.jmst.2025.07.057

来源：材料分析与应用