基于预定义蛇形裂纹的高灵敏度应变传感器,具有超低检测限。

IF 12.2 2区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Qingshi Meng, Tengfei Chi, Shuang Guo, Milad Razbin, Shuying Wu, Shuai He, Sensen Han, Shuhua Peng
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引用次数: 0

摘要

柔性和可拉伸应变传感器因其在人体健康监测和人机界面等多个领域的潜在应用而备受关注。以往的研究表明,基于微裂缝的应变传感器可以通过操纵导电薄膜内随机产生的裂缝的开合来实现高灵敏度和宽传感范围。然而,微裂纹形成的不可控性会导致传感器的性能随时间发生漂移,从而影响其精度和可靠性。在这项研究中,通过预先定义裂纹,我们推出了一种新型电阻应变传感器,它具有高灵敏度、出色的线性度、超低的检测限以及对偏轴变形的鲁棒性。该传感器的工作原理非常简单,即在精心设计的导电蛇形曲线内调制欧姆接触,并由预拉伸薄膜封装。这种设计有助于实现 495 的高测量系数、优异的线性度(R2 > 0.98)以及 0.01% 应变的超低检测阈值。此外,它还能在弯曲和扭转等离轴变形时保持性能完整性,而这些特性对于精确监测人体运动是不可或缺的。为了探索实际应用,我们模拟了一个驾驶场景,将传感器阵列安装在驾驶员的颈部。使用机器学习算法对传感器输出进行分析后,成功确定了驾驶员是否疲劳。该演示强调了我们的传感器技术在医疗保健监测、可穿戴生物力学系统和人机界面等应用领域的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Highly sensitive strain sensors with ultra-low detection limit based on pre-defined serpentine cracks.

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.

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来源期刊
Materials Horizons
Materials Horizons CHEMISTRY, MULTIDISCIPLINARY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
18.90
自引率
2.30%
发文量
306
审稿时长
1.3 months
期刊介绍: Materials Horizons is a leading journal in materials science that focuses on publishing exceptionally high-quality and innovative research. The journal prioritizes original research that introduces new concepts or ways of thinking, rather than solely reporting technological advancements. However, groundbreaking articles featuring record-breaking material performance may also be published. To be considered for publication, the work must be of significant interest to our community-spanning readership. Starting from 2021, all articles published in Materials Horizons will be indexed in MEDLINE©. The journal publishes various types of articles, including Communications, Reviews, Opinion pieces, Focus articles, and Comments. It serves as a core journal for researchers from academia, government, and industry across all areas of materials research. Materials Horizons is a Transformative Journal and compliant with Plan S. It has an impact factor of 13.3 and is indexed in MEDLINE.
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