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Wavy-Interlocked Stretchable Triboelectric Nanogenerators Enhanced by Liquid Metal Microflowers for Self-Powered
Qianqian Xu1,2, Huimin Li1, Hanmin Zeng1
1State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan, China.
None:
The increasing demand for sustainable, stretchable power sources in wearable electronics has driven the development of high-performance triboelectric nanogenerators (TENGs). Despite significant progress, stretchable TENGs continue to encounter critical challenges, including insufficient charge density, unstable output performance, and degraded durability when subject to large levels of deformation, which hinder their integration into high-performance flexible wearable systems. In this work, we construct a stretchable TENG by fabricating an interlocking wavy architecture using electrically conductive eutectic gallium-indium microflowers (EGaIn MFs) embedded into optimized dielectric membranes based on poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) and Nylon-6 nanofibers. The multi-petaled, wrinkled microflower morphology of the resulting structure is shown to increase the specific surface area and hierarchical roughness. The electron-rich properties of the EGaIn MFs promote the formation of the β-phase in PVDF-TrFE and the γ-phase in Nylon-6, and also act as localized electrically conductive "islands" which reduce the equivalent dielectric thickness and increase the overall capacitance. Based on using membranes with an optimized EGaIn MF loading level, the triboelectric layers were patterned into an interlocked wavy architecture, which expanded the effective contact area and provided a high level of stretchability. The fabricated stretchable TENG with a 30° corrugation angle exhibited a 60% tensile strain, a peak output of 188.6 V and 31.1 µA, and a sensitivity of 3.38 V per unit strain and 0.51 µA per unit strain, respectively. Finally, the device was successfully used for real-time gesture recognition, demonstrating its potential for self-powered wearable sensing and intelligent human-machine interfaces.
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