| Autor: |
Taewi Kim, Insic Hong, Minho Kim, Sunghoon Im, Yeonwook Roh, Changhwan Kim, Jongcheon Lim, Dongjin Kim, Jieun Park, Seunggon Lee, Daseul Lim, Junggwang Cho, Seokhaeng Huh, Seung-Un Jo, ChangHwan Kim, Je-Sung Koh, Seungyong Han, Daeshik Kang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
npj Flexible Electronics, Vol 7, Iss 1, Pp 1-12 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2397-4621 |
| DOI: |
10.1038/s41528-023-00255-2 |
| Popis: |
Abstract For legged robots, collecting tactile information is essential for stable posture and efficient gait. However, mounting sensors on small robots weighing less than 1 kg remain challenges in terms of the sensor’s durability, flexibility, sensitivity, and size. Crack-based sensors featuring ultra-sensitivity, small-size, and flexibility could be a promising candidate, but performance degradation due to crack growing by repeated use is a stumbling block. This paper presents an ultra-stable and tough bio-inspired crack-based sensor by controlling the crack depth using silver nanowire (Ag NW) mesh as a crack stop layer. The Ag NW mesh inspired by skin collagen structure effectively mitigated crack propagation. The sensor was very thin, lightweight, sensitive, and ultra-durable that maintains its sensitivity during 200,000 cycles of 0.5% strain. We demonstrate sensor’s feasibility by implementing the tactile sensation to bio-inspired robots, and propose statistical and deep learning-based analysis methods which successfully distinguished terrain type. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink