Highly stretchable, repeatable, and easy-to-prepare ionogel based on polyvinyl chloride for wearable strain sensors

材料科学 聚氯乙烯 重复性 增塑剂 复合材料 离子液体 弹性体 化学工程 生物医学工程 色谱法 有机化学 医学 工程类 催化作用 化学
作者
Qingsong He,Qiyun Zhong,Zheng Sun,Haoran Zhang,Zijie Zhao,Ziyan Shi,Xiaofang Liu,Zefang Zhao,Ji Lu,Yuze Ye,Yefu Wang,Yong Li,Tengfei Xiang,Jin Zhao,Yannan Xie
出处
期刊:Nano Energy [Elsevier BV]
卷期号:113: 108535-108535 被引量:46
标识
DOI:10.1016/j.nanoen.2023.108535
摘要

Conventional hydrogels are prone to water loss and instability, while non-hydrogel materials can be designed with high stability and repeatability. Therefore, a flexible sensing material was developed to meet the requirements of high elasticity, good stability, and high repeatability using a simple physical blending method. The developed ionogel comprised polyvinyl chloride (PVC) gel as the elastomer matrix, ionic liquid (IL) 1-ethyl-3-methylimidazolium thiocyanate as the dielectric, and dibutyl adipate (DBA) as the plasticizer. The PVC organic-ionogel exhibited good physicochemical stability without any phase change in the range of − 30 to 127 °C, and it had a maximum tensile strain of 424 %. Its shape and dimensions remained unchanged in air for 60 days, and the sensing performance was maintained following exposure to air for over a year. Moreover, the PVC organic-ionogel with moderate DBA and IL contents (DBA 7, IL 3 %) showed the best performance, which exhibited the best linearity with a relative error of only 0.96 %, sensitivity of 1.90, modulus of 17.71 kPa, response time of 161 ms, and continuous and stable operation for 35,000 cycles. This excellent performance enabled the PVC organic-ionogel to detect a range of human movements, namely finger flexion, throat vibration, muscle contraction, daily movements, and special limb movements, such as Tai Chi, with high accuracy and sensitivity. This work provides a new approach for the design of flexible sensing materials with variable parameters, in addition to a new solution for the development of wearable devices, sports-related risk monitoring, and boxing action optimization.
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