Rigid-flexible synergy dual-gradient microwrinkled strain sensor for machine learning-assisted wearable technology

ZM Chu and LM Fu and DS Huang and YZ Song and JY Liu and XX Fan and JF Wang and W Zhang, CHEMICAL ENGINEERING JOURNAL, 515, 163818 (2025).

DOI: 10.1016/j.cej.2025.163818

Flexible strain sensors have attracted considerable attention for their potential to accurately detect and quantify deformation information. However, the performance of present strain sensors requires a trade-off between high sensitivity and a wide strain range. To overcome this challenge, we propose a novel MXene/reduced graphene oxide (rGO)-based wavelength-thickness dual-gradient microwrinkled structure strain sensor. Microscopic morphological characterization and molecular dynamics (MD) simulations synergistically reveal the strainsensing mechanism of the force-sensitive material that operates in conjunction with the new structure. The sensor exhibits high sensitivity (110-220 % strain, GF exceeds 1200), excellent stretchability (220 %), ultra-low strain detection limit (0.02 %), sub-millisecond response (42 ms), and good reusability (10,000 cycles). The sensor can comprehensively monitor human movement, combined with high-quality signals from 13 types of gesture changes and a trained machine learning model that can accurately recognize the gesture information (98.29 % accuracy). To further validate the application potential of the sensors, a smart wearable system is constructed for real-time and wireless control of a mechanical hand that can convey different gesture information or grasp and distinguish different objects. Therefore, this sensor has broad application prospects in fields such as motion monitoring, sign language recognition, and human-machine interaction.

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