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Cited by 437

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Realizing the potential of dielectric elastomer artificial muscles

Open access
Duduta, Mihai, Hajiesmaili, Ehsan, Zhao, Huichan, Wood, Robert J., Clarke, David R.·2019·Proceedings of the National Academy of Sciences

Abstract

Significance: To build new robots that can safely interact with people, while completing complex tasks, we need artificial muscles. Ideally, these devices would be completely soft, as strong as natural muscles, and powered by electricity for easy integration with the rest of the robot. We found a materials solution that relies on an established technology, a dielectric elastomer soft capacitor, which deforms when an electric field is applied. By using a unique combination of nanoscale conductive particles and soft elastomers, we can apply high electric fields and reach contraction forces on par with natural muscles. Potential uses include novel surgical tools, prosthetics and artificial limbs, haptic devices, and more capable soft robots for exploration.

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Registry

Crossref · 10.1073/pnas.1815053116

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Metadata

Authors
Duduta, M., Hajiesmaili, E., Zhao, H., Wood, R. J., & Clarke, D. R.
Venue
Proceedings of the National Academy of Sciences
Published
2019-01-24
Volume
116
Issue
7
Pages
2476-2481
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2911887105
Cited by
437
References
3
FWCI
14.68
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Realizing the potential of dielectric elastomer artificial muscles. https://app.cochat.ai/papers/10.1073/pnas.1815053116
The paper
Duduta, M., Hajiesmaili, E., Zhao, H., Wood, R. J., & Clarke, D. R. (2019). Realizing the potential of dielectric elastomer artificial muscles. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1815053116

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.