题目： | Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification |
作者： | Lingjie Xie1,2,3#, Hao Lei1,3,4#, Yina Liu2*, Bohan Lu2, Xuan Qin1, Chengyi Zhu1, Haifeng Ji1, Zhenqiu Gao1, Yifan Wang2, Yangyang Lv1, Chun Zhao4, Ivona Z Mitrovic3, Xuhui Sun1 and Zhen Wen1* |
单位： | 1Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, P. R. China. 2Department of Applied Mathematics, School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China. 3Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK. 4Department of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China. |
摘要： | The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin–sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young’s moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5 V kPa−1 with a detection limit of 0.14 Pa. The self-adhesive hydrogel is developed to strengthen the sensor–skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%. |
影响因子： | 27.4 |
分区情况： | 一区 |
链接： | https://doi.org/10.1002/adma.202406235 责任编辑：杜欣 |