柔性壓力傳感器因能精確檢測外部壓力并貼合復雜曲面，在健康監測與人機交互領域備受關注。然而，靈敏度、檢測范圍與機械穩定性之間的固有權衡嚴重制約了其性能提升與進一步發展。研究采用濕法紡絲與基底輔助干燥策略，通過調控石墨烯纖維（GF）的形態結構優化導電網絡。最終成功制備出高導電性（3.19×10? S m?1）、抗拉強度達179.6 MPa、應變能力達6.5%的致密帶狀GF，并將其應用于柔性壓力傳感器的傳感層。

該基石碳纖維壓力傳感器在0-10kPa范圍內展現出30.79kPa?1的高靈敏度，即使檢測范圍擴展至150kPa仍保持15.59kPa?1的靈敏度。此外，其響應/恢復特性迅捷（88毫秒/72毫秒），經10000次循環測試仍保持優異耐久性。該傳感器能有效檢測關節彎曲、腕部屈曲及脈搏跳動等細微生理信號，彰顯其在健康監測與智能可穿戴設備領域的應用潛力。

2圖文導讀

圖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.

圖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.

圖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.

圖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.

圖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.

圖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小結

綜上所述，成功制備了基于雙面高密度玻璃纖維（GF）的高性能柔性壓力傳感器。通過采用濕法紡絲技術結合基板輔助干燥策略，優化了GF的形態和層間堆疊結構，從而獲得了高電導率（3.19×10? S m?1）和優異的機械性能（抗拉強度179.6 MPa，斷裂應變6.5%）。制備的GF壓力傳感器在0-10 kPa壓力范圍內展現出30.79 kPa?1的超高靈敏度，當檢測范圍擴展至150 kPa時仍保持15.59 kPa?1的高靈敏度。此外，該器件展現出快速響應/恢復特性（響應時間88毫秒/恢復時間72毫秒），并在經歷10000次加載循環后仍保持穩定的傳感性能。該傳感器能精確捕捉人體運動信號，如指關節彎曲、喉部振動和脈搏搏動，凸顯其在健康監測、智能可穿戴設備及人機交互領域的巨大潛力。本研究為柔性傳感材料設計提供了新思路，為高性能壓阻式傳感器的開發奠定了基礎。

文獻：

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

來源：材料分析與應用