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Autonomous self-healing and stretchable triboelectric nanogenerator with hybrid double-network elastomer for self-powered multifunctional electronics
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Pandey, Puran | - |
| dc.contributor.author | Seo, Min-Kyu | - |
| dc.contributor.author | Jo, Seunghwan | - |
| dc.contributor.author | Shrestha, Kumar | - |
| dc.contributor.author | Lee, Juwon | - |
| dc.contributor.author | Sohn, Jung Inn | - |
| dc.date.accessioned | 2025-10-15T05:00:07Z | - |
| dc.date.available | 2025-10-15T05:00:07Z | - |
| dc.date.issued | 2025-10 | - |
| dc.identifier.issn | 2522-0128 | - |
| dc.identifier.issn | 2522-0136 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/61752 | - |
| dc.description.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. | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Springer Nature Switzerland AG | - |
| dc.title | Autonomous self-healing and stretchable triboelectric nanogenerator with hybrid double-network elastomer for self-powered multifunctional electronics | - |
| dc.type | Article | - |
| dc.publisher.location | 스위스 | - |
| dc.identifier.doi | 10.1007/s42114-025-01479-8 | - |
| dc.identifier.scopusid | 2-s2.0-105017564891 | - |
| dc.identifier.wosid | 001586623400028 | - |
| dc.identifier.bibliographicCitation | Advanced Composites and Hybrid Materials, v.8, no.5 | - |
| dc.citation.title | Advanced Composites and Hybrid Materials | - |
| dc.citation.volume | 8 | - |
| dc.citation.number | 5 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
| dc.relation.journalResearchArea | Materials Science | - |
| dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
| dc.relation.journalWebOfScienceCategory | Materials Science, Composites | - |
| dc.subject.keywordAuthor | Self-healing Ecoflex | - |
| dc.subject.keywordAuthor | Double network elastomer | - |
| dc.subject.keywordAuthor | Self-healing TENG | - |
| dc.subject.keywordAuthor | Handwriting recognition | - |
| dc.subject.keywordAuthor | Deep learning | - |
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