Understanding the sensing performance alteration mechanism of a Yarn-based strain sensor after encapsulation and an effective encapsulation structural designs

封装(网络) 材料科学 纳米技术 纱线 计算机科学 复合材料 计算机网络
作者
Fei Huang,Chen Huang,Fenye Meng,Kean C. Aw,Xiong Yan,Jiyong Hu
出处
期刊:Colloids and Surfaces A: Physicochemical and Engineering Aspects [Elsevier BV]
卷期号:697: 134501-134501
标识
DOI:10.1016/j.colsurfa.2024.134501
摘要

Microcrack-based yarn strain sensors with non-uniform and rough structures offer high sensitivity and flexibility, making them promising for wearable electronics. However, their low mechanical endurance limits their usability. Encapsulating is a common method used to protect the conductive network and enhance environmental stability, but its impact on sensing performance is poorly understood. This work investigates the effects of thickness and tensile modulus of conformal encapsulation layer on the performance of double-threaded conductive yarns (CNT/DTY), especially focusing on the thickness variation coefficient of the conformal encapsulation layer. The results showed that the encapsulation layer affects the mechanical and electrical properties of yarn sensors. The permeation of Ecoflex transforms the conductive layer into Ecoflex/CNT composites, increasing the sensor's initial electrical resistance. The encapsulation layer changes the rate of strain transfer from the substrate to the conductive layer, slowing strain localization. Increasing the thickness variation coefficient of the encapsulation layer improves the maximum strain range, linearity and repeatability, while decreasing the sensitivity and electromechanical hysteresis. An encapsulation layer with higher tensile modulus significantly reduces sensitivity, linearity and increases electromechanical hysteresis. Optimizing the encapsulation layer not only provides the sensors with robust mechanical support and protection but also enhance its sensing properties, including excellent water resistance. Moreover, encapsulated yarn sensors showed good potential in joint motion monitoring in water, gait analysis, and gesture recognition for wearable applications.
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