Evidence map›Paper›PMID 36771853›Full record

ArticlePolymers2023

Self-Aware Artificial Coiled Yarn Muscles with Enhanced Electrical Conductivity and Durability via a Two-Step Process.

Yongqi Gong, Wanyi Chen, Jianyang Li, Shun Zhao, Luquan Ren, Kunyang Wang, Bingqian Li

Abstract read
In one paragraph

Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yongqi GongKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
Wanyi ChenState Grid Jilin Electric Power Co., Ltd., Changchun 130022, China.
Jianyang LiKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
Shun ZhaoKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
Luquan RenKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.
Kunyang WangKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.ORCID 0000-0003-3807-3697
Bingqian LiKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China.

Funding

China Postdoctoral Science Foundation 2022M711299National Natural Science Foundation of China 52005209National Natural Science Foundation of China 52021003National Natural Science Foundation of China 91948302Natural Science Foundation of Jilin Province 20210101053JC
6 · The paper itself

Abstract

Muscles are capable of modulating the body and adapting to environmental changes with a highly integrated sensing and actuation. Inspired by biological muscles, coiled/twisted fibers are adopted that can convert volume expansion into axial contraction and offer the advantages of flexibility and light weight. However, the sensing-actuation integrated fish line/yarn-based artificial muscles are still barely reported due to the poor actuation-sensing interface with off-the-shelf fibers. We report herein artificial coiled yarn muscles with self-sensing and actuation functions using the commercially available yarns. Via a two-step process, the artificial coiled yarn muscles are proved to obtain enhanced electrical conductivity and durability, which facilitates the long-term application in human-robot interfaces. The resistivity is successfully reduced from 172.39 Ω·cm (first step) to 1.27 Ω·cm (second step). The multimode sense of stretch strain, pressure, and actuation-sensing are analyzed and proved to have good linearity, stability and durability. The muscles could achieve a sensitivity (gauge factor, GF) of the contraction strain perception up to 1.5. We further demonstrate this self-aware artificial coiled yarn muscles could empower non-active objects with actuation and real-time monitoring capabilities without causing damage to the objects. Overall, this work provides a facile and versatile tool in improving the actuation-sensing performances of the artificial coiled yarn muscles and has the potential in building smart and interactive soft actuation systems.

Indexed as

artificial musclecoiled yarnintegrationself-awaresoft actuation

Identifiers

PMID36771853
PMCPMC9921128

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.