{"title":"Highly sensitive strain sensors with ultra-low detection limit based on pre-defined serpentine cracks.","authors":"Qingshi Meng, Tengfei Chi, Shuang Guo, Milad Razbin, Shuying Wu, Shuai He, Sensen Han, Shuhua Peng","doi":"10.1039/d4mh01136h","DOIUrl":null,"url":null,"abstract":"<p><p>Flexible and stretchable strain sensors have garnered significant interest due to their potential applications in various fields including human health monitoring and human-machine interfaces. Previous studies have shown that strain sensors based on microcracks can exhibit both high sensitivity and a wide sensing range by manipulating the opening and closing of randomly generated cracks within conductive thin films. However, the uncontrolled nature of microcrack formation can cause a drift in the sensor's performance over time, affecting its accuracy and reliability. In this study, by pre-defining the cracks, we introduce a novel resistive strain sensor with high sensitivity, excellent linearity, an ultra-low detection limit, and robustness against off-axis deformation. The sensor operates on a simple mechanism involving the modulation of ohmic contact within intricately designed conductive serpentine curves, which are encapsulated by pre-stretched thin films. This design facilitates a high gauge factor of 495, exceptional linearity (<i>R</i><sup>2</sup> > 0.98), and an ultra-low detection threshold of 0.01% strain. Moreover, it maintains performance integrity during off-axis deformations such as bending and twisting, features that are indispensable for accurately monitoring human motion. To explore practical applications, a driving scenario was simulated where a sensor array was positioned on the driver's neck. The sensor output was analyzed using machine learning algorithms to successfully determine the presence of driver fatigue. This demonstration underlines the potential of our sensor technology in applications ranging from healthcare monitoring to wearable biomechanical systems and human-machine interfaces.</p>","PeriodicalId":87,"journal":{"name":"Materials Horizons","volume":" ","pages":""},"PeriodicalIF":12.2000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Horizons","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1039/d4mh01136h","RegionNum":2,"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
Flexible and stretchable strain sensors have garnered significant interest due to their potential applications in various fields including human health monitoring and human-machine interfaces. Previous studies have shown that strain sensors based on microcracks can exhibit both high sensitivity and a wide sensing range by manipulating the opening and closing of randomly generated cracks within conductive thin films. However, the uncontrolled nature of microcrack formation can cause a drift in the sensor's performance over time, affecting its accuracy and reliability. In this study, by pre-defining the cracks, we introduce a novel resistive strain sensor with high sensitivity, excellent linearity, an ultra-low detection limit, and robustness against off-axis deformation. The sensor operates on a simple mechanism involving the modulation of ohmic contact within intricately designed conductive serpentine curves, which are encapsulated by pre-stretched thin films. This design facilitates a high gauge factor of 495, exceptional linearity (R2 > 0.98), and an ultra-low detection threshold of 0.01% strain. Moreover, it maintains performance integrity during off-axis deformations such as bending and twisting, features that are indispensable for accurately monitoring human motion. To explore practical applications, a driving scenario was simulated where a sensor array was positioned on the driver's neck. The sensor output was analyzed using machine learning algorithms to successfully determine the presence of driver fatigue. This demonstration underlines the potential of our sensor technology in applications ranging from healthcare monitoring to wearable biomechanical systems and human-machine interfaces.