用于自供电多功能电子器件的混合双网络弹性体自主自修复和可拉伸摩擦电纳米发电机

IF 21.8 2区 材料科学 Q1 MATERIALS SCIENCE, COMPOSITES
Puran Pandey, Min-Kyu Seo, Seunghwan Jo, Kumar Shrestha, Juwon Lee, Jung Inn Sohn
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引用次数: 0

摘要

尽管人们对用于自供电可穿戴电子产品的摩擦电纳米发电机(TENGs)产生了广泛的兴趣,但开发有效结合自修复和强大机械性能的TENGs仍然具有挑战性。在此,我们报告了一种具有优异机械性能的自主完全自愈TENG (SH−TENG),可用于多功能自供电应用。SH - TENG是用一种自修复的Ecoflex (SH - Ecoflex)制成的,该材料是由Ecoflex -聚硼硅氧烷(PBS)杂化双网弹性体聚合而成的。SH - Ecoflex具有高拉伸强度、优异的拉伸性(590%)和自主机械自愈效率(2小时内68%)。SH−TENG高效地收集机械能(269.1 mW/m2),即使在损坏或机械变形后也能自主恢复其性能,并在12,000次接触分离循环中保持持久性能。SH−TENG在短时间内有效地为电容充电,为数字温湿度计供电,并提供自供电传感功能,以监测人体关节运动。此外,手写触控面板采用了基于对角条状空隙电极的SH - TENG设计,以增强手指滑动的感觉,并为每个手写字母产生独特的电信号。通过集成深度学习模型,开发了一种先进的手写识别系统,可以识别5个手写字母,平均准确率达到99%,展示了其在智能触觉感知和人机交互以及签名和用户识别系统中的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous self-healing and stretchable triboelectric nanogenerator with hybrid double-network elastomer for self-powered multifunctional electronics

Despite the widespread interest in triboelectric nanogenerators (TENGs) for self-powered wearable electronics, the development of TENGs that effectively combine self-healing and robust mechanical properties remains challenging. Herein, we report an autonomous fully self-healing TENG (SH − TENG) with excellent mechanical properties for multifunctional self-powered applications. The SH − TENG is fabricated using a self-healing Ecoflex (SH − Ecoflex) synthesized through the polymerization of an Ecoflex–polyborosiloxane (PBS) hybrid double network elastomer. The SH − Ecoflex exhibits high tensile strength, exceptional stretchability (590%), and autonomous mechanical self-healing efficiency (68% in 2 h). The SH − TENG efficiently harvests mechanical energy (269.1 mW/m2), autonomously recovers its performance even after damage or mechanical deformation, and maintains durable performance over 12,000 contact-separation cycles. The SH − TENG effectively charges the capacitor within a short time to power the digital thermo-hygrometer, and offers self-powered sensing functionality to monitor human joint movements. Furthermore, the handwriting touch panel is designed with a diagonal strip-void electrode-based SH − TENG to enhance the perception of finger sliding and generate a distinct electrical signal for each handwritten letter. Through the integration of a deep learning model, an advanced handwriting recognition system has been developed to recognize five handwritten letters with an average accuracy of 99%, demonstrating its potential for future applications in intelligent tactile perception and human–machine interaction, as well as signature and user recognition systems.

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来源期刊
CiteScore
26.00
自引率
21.40%
发文量
185
期刊介绍: Advanced Composites and Hybrid Materials is a leading international journal that promotes interdisciplinary collaboration among materials scientists, engineers, chemists, biologists, and physicists working on composites, including nanocomposites. Our aim is to facilitate rapid scientific communication in this field. The journal publishes high-quality research on various aspects of composite materials, including materials design, surface and interface science/engineering, manufacturing, structure control, property design, device fabrication, and other applications. We also welcome simulation and modeling studies that are relevant to composites. Additionally, papers focusing on the relationship between fillers and the matrix are of particular interest. Our scope includes polymer, metal, and ceramic matrices, with a special emphasis on reviews and meta-analyses related to materials selection. We cover a wide range of topics, including transport properties, strategies for controlling interfaces and composition distribution, bottom-up assembly of nanocomposites, highly porous and high-density composites, electronic structure design, materials synergisms, and thermoelectric materials. Advanced Composites and Hybrid Materials follows a rigorous single-blind peer-review process to ensure the quality and integrity of the published work.
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