Wang Zihan, Wang Shenglong, Lan Boling, Sun Yue, Huang Longchao, Ao Yong, Li Xuelan, Jin Long, Yang Weiqing, Deng Weili
Key Laboratory of Advanced Technologies of Materials (Ministry of Education), School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China.
Research Institute of Frontier Science, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China.
Nanomicro Lett. 2025 Apr 29;17(1):241. doi: 10.1007/s40820-025-01757-6.
Bimodal pressure sensors capable of simultaneously detecting static and dynamic forces are essential to medical detection and bio-robotics. However, conventional pressure sensors typically integrate multiple operating mechanisms to achieve bimodal detection, leading to complex device architectures and challenges in signal decoupling. In this work, we address these limitations by leveraging the unique piezotronic effect of Y-ion-doped ZnO to develop a bimodal piezotronic sensor (BPS) with a simplified structure and enhanced sensitivity. Through a combination of finite element simulations and experimental validation, we demonstrate that the BPS can effectively monitor both dynamic and static forces, achieving an on/off ratio of 1029, a gauge factor of 23,439 and a static force response duration of up to 600 s, significantly outperforming the performance of conventional piezoelectric sensors. As a proof-of-concept, the BPS demonstrates the continuous monitoring of Achilles tendon behavior under mixed dynamic and static loading conditions. Aided by deep learning algorithms, the system achieves 96% accuracy in identifying Achilles tendon movement patterns, thus enabling warnings for dangerous movements. This work provides a viable strategy for bimodal force monitoring, highlighting its potential in wearable electronics.
能够同时检测静态和动态力的双峰压力传感器对于医学检测和生物机器人技术至关重要。然而,传统的压力传感器通常集成多种操作机制以实现双峰检测,这导致了复杂的器件架构和信号解耦方面的挑战。在这项工作中,我们通过利用Y离子掺杂的ZnO独特的压电子效应来解决这些限制,开发了一种结构简化且灵敏度增强的双峰压电子传感器(BPS)。通过有限元模拟和实验验证相结合,我们证明BPS能够有效地监测动态和静态力,实现1029的开/关比、23439的应变系数以及高达600秒的静态力响应持续时间,显著优于传统压电传感器的性能。作为概念验证,BPS展示了在动态和静态混合加载条件下对跟腱行为的连续监测。在深度学习算法的辅助下,该系统在识别跟腱运动模式方面达到了96%的准确率,从而能够对危险动作发出警告。这项工作为双峰力监测提供了一种可行的策略,突出了其在可穿戴电子设备中的潜力。