Polarization‐Induced Mechanically Socketed Ultra‐Stretchable and Breathable Textile‐Based Nanogenerator and Pressure Sensor

材料科学 纳米发生器 压力传感器 极化(电化学) 织物 复合材料 光电子学 纳米技术 机械工程 压电 工程类 物理化学 化学
作者
Romy Garg,Abhisek Majhi,P. Nagasreenivasarao,Nikhil Ram Patra,Ritesh Barve,Kaushik Parida
出处
期刊:Advanced Functional Materials [Wiley]
卷期号:34 (32) 被引量:2
标识
DOI:10.1002/adfm.202401593
摘要

Abstract Textile‐based wearable triboelectric nanogenerators (TENGs) have emerged as viable power sources for wearable electronics. However, it is still a daunting challenge to realize a high‐performing textile‐based nanogenerator without compromising its intrinsic textile‐like properties, such as stretchability, breathability, and conformability. The above challenge is addressed by fabricating a record high‐performing wearable, breathable and stretchable nanogenerator based on polarization‐induced ultra‐stretchable EVA/Nylon‐11 micro/nano‐fibers (1250%) as the triboelectric positive layer and polarization‐induced ultra‐stretchable EVA/PVDF/BA 2 CsAgBiBr 7 micro/nano‐fibers (1480%) as the triboelectric negative layer. The excellent stretchability, breathability (1.15 Kgm −2 d −1 ) and high energy‐harvesting performance (650 V, 19.5 µAcm −2 ) are attributed to the mechanically‐socketed structure of the aligned stretchable EVA microfibers with the polarized nanofibers, thus providing a universal framework to fabricate stretchable piezoelectric fibers. Compositional engineering and polarization‐induced surface charges coupled with triboelectric‐induced surface charges enabled enhanced performance compared to other wearable stretchable textile‐based nanogenerators. Additionally, it is utilized as a stretchable self‐powered pressure sensor with an ultra‐wide pressure sensing range (0.05–500 kPa) and high sensitivity (2.5 VkPa −1 ) even under deformations. To the best of the knowledge, PI‐STENG stands out amongst various stretchable self‐powered pressure sensors in terms of pressure sensing range and stretchability. The PI‐STENG is demonstrated as a machine learning‐enabled intellisensor for workforce safety monitoring.
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