Seamlessly Integrated, High‐Output and Robust Triboelectric Nanoyarns: Continuously Electrospun Core‐Sheath Architectures for Multimodal Sensing Platforms

作者
Yiqun Chen,Jinrong Huang,Zixuan Liu,Zixuan Liu,Yi Zou,Jianwen Chen,Zunfeng Liu,Zunfeng Liu,Yutian Zhu
出处
期刊:Advanced Functional Materials [Wiley]
标识
DOI:10.1002/adfm.202523166
摘要

Abstract Textile‐based triboelectric nanogenerators (TENGs) have emerged as a transformative technology for self‐powered wearable electronics. However, current textile‐based TENGs predominantly employ multilayer architectures that introduce critical trade‐offs: elevated mechanical rigidity, compromised air permeability, and interfacial instability. More fundamentally, their 2D geometry impedes textile‐process integration and restricts dynamic biomechanical compatibility. To overcome these issues, a coaxial nanofiber yarn‐based single‐electrode TENG through a scalable one‐step electrospinning that eliminates multilayer assembly via multiscale engineered core‐sheath integration is developed. This approach achieves performance integration across multiple scales: through incorporating Cs 3 Bi 2 Cl 9 into poly(vinylidene fluoride‐co‐trifluoroethylene) (PVDF‐TrFE) to enhance molecular‐scale β‐phase and dielectric properties, optimizing surface roughness at the micro/nano‐scale, and forming a macroscale core‐sheath structure with a triboelectric PVDF‐TrFE/Cs 3 Bi 2 Cl 9 sheath. The resulting TENG demonstrates enhanced output performance and operational stability, thus enabling multifunctional applications including interactive guzheng digitization interfaces, textile classification with 95.83% accuracy, and self‐powered Morse communication systems. When woven into a 2D fabric, this TENG achieves high‐pressure sensitivity (3.64 V kPa −1 ), broad detection range (up to 50 kPa), extreme durability (>50 000 cycles), as well as inherent breathability, biocompatibility, and industrial washability. This work establishes a high‐performance yarn‐based TENG through scalable electrospinning, demonstrating practical applications in smart musical instruments and machine learning‐enhanced fabric identification systems.
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