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Flexible pressure sensors have garnered significant attention over the past few decades owing to their indispensable role in electronic skin and health monitoring, and there is an urgent demand for high sensitivity to meet the requirements of large-scale applications. In this work, we demonstrate a resistive pressure sensor with self-assembled MXene/MWCNTs complex conductive networks, whose hollow substrate is achieved via designed molds and thermally expandable microspheres. Herein, the pressure sensor exhibits the desired performances, including a high sensitivity of 2.63 kPa−1, an ultra-low detection limit of ~0.25% relative resistance change, and rapid response times of 340 ms. The high performance enables promising prospects for detecting diverse human body movements. More importantly, it has been applied in numerical classification based on machine learning with the Hidden Markov Model, achieving an impressive accuracy of ~99.2%. Our research offers novel insights for enhancing the performance of pressure sensors, which hold great potential for practical applications.
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Wang et al. (2025) studied this question.
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