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

Journal 2025 · IF 19 · 发表于 2025 年 11 月 20 日
作者: Yiqun Chen,Jinrong Huang,Zixuan Liu,Yi Zou,Jianwen Chen,Zunfeng Liu,Yutian Zhu
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摘要

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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