Lingjie Xie, Hao Lei, Yina Liu, Bohan Lu, Xuan Qin, Chengyi Zhu, Haifeng Ji, Zhenqiu Gao, Yifan Wang, Yangyang Lv, Chun Zhao, Ivona Z Mitrovic, Xuhui Sun, Zhen Wen
{"title":"Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification.","authors":"Lingjie Xie, Hao Lei, Yina Liu, Bohan Lu, Xuan Qin, Chengyi Zhu, Haifeng Ji, Zhenqiu Gao, Yifan Wang, Yangyang Lv, Chun Zhao, Ivona Z Mitrovic, Xuhui Sun, Zhen Wen","doi":"10.1002/adma.202406235","DOIUrl":null,"url":null,"abstract":"<p><p>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<sup>-1</sup> 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%.</p>","PeriodicalId":114,"journal":{"name":"Advanced Materials","volume":null,"pages":null},"PeriodicalIF":27.4000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/adma.202406235","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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%.
期刊介绍:
Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.