An Ultrahigh-Strength Braided Smart Yarn for Wearable Individual Sensing and Protection
Advanced Fiber Materials(2024)
摘要
The insufficient comprehensive mechanical properties and inadequate flexibility of wearable sensors limit their body-protection capability, durability, and comfort. There are challenges in using flexible wearable devices for high-performance practical applications, especially on large scales. Here, an ultrahigh-strength ultra-high-molecular-weight polyethylene braided smart yarn (UBSY) has been designed and mass produced. It is based on triboelectric nanogenerators and prepared by combining commercial ultra-high-molecular-weight polyethylene yarn and conductive yarn with a cored biaxial braided structure. Structural parameters, including the ultra-high-molecular-weight polyethylene yarn diameter, twist, and braiding pitch, are optimized to balance the mechanical properties and electrical outputs. The prepared UBSYs are characterized based on a range of reliable properties, including ultrahigh tensile strength (194.83 N), excellent abrasive resistance (up to 306 abrasive cycles), great hydrophobicity (water contact angle of 115.49°), acid and alkali splash resistance, and decent triboelectric outputs (1.5 V, 3.0 nA, and 0.5 nC). An intelligent weft-knitted textile wearable sensor is fabricated with UBSY using a matured flat-knitting technique, which provides excellent mechanical strength, physical protection and comfort. Furthermore, a pair of smart elbow guards have been demonstrated to highlight UBSY-based wearable sensors’ potential in outdoor sports management. In addition, equipped with a satisfactory body protective capacity against various risks and matured preparation technologies, the UBSY-based wearable sensor provides a practical solution for large-scale applications of high-performance motion sensing in complex environments.
更多查看译文
关键词
Braided smart yarn,Wearable sensor,Outdoor sports management,High performance,Triboelectric nanogenerator
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要