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Biodegradable single-electrode triboelectric nanogenerator for self-powered robotic texture sensing
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hajra, Sugato | - |
| dc.contributor.author | Pal, Shibam | - |
| dc.contributor.author | Kaja, Kushal Ruthvik | - |
| dc.contributor.author | Choi, Yoobin | - |
| dc.contributor.author | Panda, Swati | - |
| dc.contributor.author | Panigrahi, Basanta Kumar | - |
| dc.contributor.author | Kim, Hoe Joon | - |
| dc.contributor.author | Wistrand, Anna Finne | - |
| dc.date.accessioned | 2026-07-30T17:40:11Z | - |
| dc.date.available | 2026-07-30T17:40:11Z | - |
| dc.date.created | 2026-06-08 | - |
| dc.date.issued | 2026-07 | - |
| dc.identifier.issn | 2052-1537 | - |
| dc.identifier.uri | https://scholar.dgist.ac.kr/handle/20.500.11750/60535 | - |
| dc.description.abstract | Human tactile acuity relies on the microstructured morphology of the fingertips, which enables sensitive detection of fine surface features during object manipulation. While triboelectric-based self-powered object recognition has gained much attention, conventional triboelectric materials are typically non-biodegradable, contributing to persistent electronic waste. This work focuses on fabricating biodegradable triboelectric interfaces for intelligent robotic texture perception and sustainable energy harvesting. Three biodegradable polymers, polylactide (PLA), poly(epsilon-caprolactone) (PCL), and poly(lactide-co-trimethylene carbonate) (PTMC), were evaluated as negative triboelectric layers against an aluminum electrode to form a single-electrode triboelectric nanogenerator (TENG). The PCL/Al TENG achieved a superior electrical output of 118 V and 772 nA, with a peak power of 24.5 & micro;W at 200 M Omega, primarily due to its higher surface roughness enhancing charge transfer. The powering of the low-power electronics and charging of the capacitors using the TENG was demonstrated. In addition, the platform was integrated into a robotic gripper for real-time texture recognition. Combined with a convolutional neural network (CNN), the system achieved 96.9% classification accuracy across eight distinct textures. This sustainable platform reduces environmental impact by using degradable materials while maintaining the mechanical robustness required for advanced robotic sensing. | - |
| dc.language | English | - |
| dc.publisher | ROYAL SOC CHEMISTRY | - |
| dc.title | Biodegradable single-electrode triboelectric nanogenerator for self-powered robotic texture sensing | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1039/d6qm00202a | - |
| dc.identifier.wosid | 001776327300001 | - |
| dc.identifier.scopusid | 2-s2.0-105040071381 | - |
| dc.identifier.bibliographicCitation | MATERIALS CHEMISTRY FRONTIERS, v.10, no.14, pp.2322 - 2332 | - |
| dc.description.isOpenAccess | TRUE | - |
| dc.citation.endPage | 2332 | - |
| dc.citation.number | 14 | - |
| dc.citation.startPage | 2322 | - |
| dc.citation.title | MATERIALS CHEMISTRY FRONTIERS | - |
| dc.citation.volume | 10 | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Chemistry; Materials Science | - |
| dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary; Materials Science, Multidisciplinary | - |
| dc.type.docType | Article | - |
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