Functionally graded nanocomposite sensors for high-fidelity biomechanical monitoring

Flexible pressure sensors are critical for next-generation wearable electronics and human-machine interfaces but remain constrained by trade-offs among gauge factor (GF), range, and mechanical compliance. Here, we report a high-performance pressure sensor based on functionally graded nanocomposites...

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Veröffentlicht in:Sensors and actuators. A. Physical. Jg. 397; S. 117228
Hauptverfasser: Jia, Xiaoli, Sun, Shilong, Miao, Yan, Liu, Lu, Ke, Liaoliang, Yang, Jie, Kitipornchai, Sritawat
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.01.2026
Schlagworte:
Biomechanical monitoring
Flexible pressure sensor
Functionally graded nanocomposites
Machine learning classification
Flexible pressure sensor
Functionally graded nanocomposites
Machine learning classification
Biomechanical monitoring
ISSN:0924-4247
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Zusammenfassung:Flexible pressure sensors are critical for next-generation wearable electronics and human-machine interfaces but remain constrained by trade-offs among gauge factor (GF), range, and mechanical compliance. Here, we report a high-performance pressure sensor based on functionally graded nanocomposites of carbon black (CB), multi-walled carbon nanotubes (MWCNTs), and polydimethylsiloxane (PDMS). Systematic orthogonal design identified optimal filler ratios, enabling the fabrication of exponential gradient structures with spatially modulated stiffness and conductivity. The resulting sensors exhibit ultrahigh GF (18.71 kPa⁻¹), a low detection limit (15 Pa), and rapid response (80/86 ms). Integrated with a random forest classifier, the sensors accurately detect cervical posture (98.33 %) and hand gestures (83.88 %), highlighting their potential for real-time biomechanical monitoring and intelligent wearable systems. [Display omitted] •Establishment of a gradient-structured sensing framework based on systematic orthogonal design.•Integration of hybrid nanofillers to form interpenetrating conductive networks with enhanced performance tunability.•Demonstration of superior sensitivity–linearity trade-offs and dynamic stability through architectural control.•Real-world application in biomechanical classification tasks using machine learning methods.
ISSN:0924-4247
DOI:10.1016/j.sna.2025.117228