Abstract
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.
| Original language | English |
|---|---|
| Article number | 389 |
| Journal | Advanced Composites and Hybrid Materials |
| Volume | 8 |
| Issue number | 5 |
| DOIs | |
| State | Published - Oct 2025 |
Keywords
- Deep learning
- Double network elastomer
- Handwriting recognition
- Self-healing Ecoflex
- Self-healing TENG
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