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Catalytically powered dynamic assembly of rod-shaped nanomotors and passive tracer particles

Open access
Wang, Wei, Duan, Wentao, Sen, Ayusman, Mallouk, Thomas E.·2013·Proceedings of the National Academy of Sciences

Abstract

Significance: Microscale catalyst particles suspended in fluids can convert the energy of chemical reactions that occur on their surfaces to movement. Collections of particles undergoing powered motion exhibit behavior that mimics living microparticles such as bacteria: swarming, predator–prey interactions, and chemotaxis. These behaviors originate from pairwise interactions of particles that so far have not been measured or understood. In this article, short-range attractive interactions of catalytic nanorod motors were shown to arise from electrical forces. These forces drive the transient formation of dimers and trimers, and are also responsible for the collection of passive tracer particles at the ends of the nanorods. The quantitative description of these interactions lays the theoretical groundwork for understanding and manipulating the collective behavior of nanomotors.

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Metadata

Authors
Wang, W., Duan, W., Sen, A., & Mallouk, T. E.
Venue
Proceedings of the National Academy of Sciences
Published
2013-10-14
Volume
110
Issue
44
Pages
17744-17749
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1984070585
Cited by
201
References
0
FWCI
10.55
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Catalytically powered dynamic assembly of rod-shaped nanomotors and passive tracer particles. https://app.cochat.ai/papers/10.1073/pnas.1311543110
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Wang, W., Duan, W., Sen, A., & Mallouk, T. E. (2013). Catalytically powered dynamic assembly of rod-shaped nanomotors and passive tracer particles. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1311543110

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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.