Issue 30, 2025

Gradient CNT/PMN-PT/PVDF piezoelectric composites for gait monitoring during weight-bearing walking

Abstract

Wearable piezoelectric sensors have gained significant attention for real-time biomechanical monitoring applications, yet existing designs often suffer from limited sensitivity, durability, and dynamic response. To address these challenges, we develope a wearable sensor utilizing gradient-architected CNT/PMN-PT/PVDF piezoelectric composites for continuous gait monitoring during weight-bearing walking. The sensor features a dual-filler gradient configuration within a poly(vinylidene fluoride) (PVDF) matrix, in which strategically distributed carbon nanotubes (CNTs) and lead magnesium niobate–lead titanate (PMN-PT) ceramic particles synergistically enhance electromechanical coupling efficiency. The CNT-enriched surface layer boosts polarization by enhancing charge injection efficiency, while the gradient-arranged PMN-PT fillers induce stress concentration, further amplifying the sensor's piezoelectric output. As a result, the sensor exhibits exceptional performance, with a decent piezoelectric coefficient Image ID:d5nr02020d-t1.gif and high sensitivity (172 mV N−1). Both experimental tests and finite element simulations validate the superior performance of this gradient structure, making it highly effective for real-time kinematic monitoring during weight-bearing walking. This composite-based sensor represents a promising advancement in wearable health technology, with immediate applications in clinical gait analysis, rehabilitation monitoring and sports injury prevention.

Graphical abstract: Gradient CNT/PMN-PT/PVDF piezoelectric composites for gait monitoring during weight-bearing walking

Supplementary files

Article information

Article type
Paper
Submitted
15 May 2025
Accepted
29 Jun 2025
First published
01 Jul 2025

Nanoscale, 2025,17, 17658-17668

Gradient CNT/PMN-PT/PVDF piezoelectric composites for gait monitoring during weight-bearing walking

W. Deng, T. Zhou, W. Zeng, Z. Wang, Y. Liu, B. Lan, S. Wang, Y. Ao, Y. Sun, S. Wang, Z. Li, L. Jin and W. Yang, Nanoscale, 2025, 17, 17658 DOI: 10.1039/D5NR02020D

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