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Cited by 669

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Nonlinear damping in mechanical resonators made from carbon nanotubes and graphene

Open access
Eichler, A., Moser, J., Chaste, J., Zdrojek, M., Wilson-Rae, I., Bachtold, A.·2011·Nature Nanotechnology

Abstract

The theory of damping is discussed in Newton's Principia and has been tested in objects as diverse as the Foucault pendulum, the mirrors in gravitational-wave detectors and submicrometre mechanical resonators. In general, the damping observed in these systems can be described by a linear damping force. Advances in nanofabrication mean that it is now possible to explore damping in systems with one or more atomic-scale dimensions. Here we study the damping of mechanical resonators based on carbon nanotubes and graphene sheets. The damping is found to strongly depend on the amplitude of motion, and can be described by a nonlinear rather than a linear damping force. We exploit the nonlinear nature of damping in these systems to improve the figures of merit for both nanotube and graphene resonators. For instance, we achieve a quality factor of 100,000 for a graphene resonator.

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Crossref · 10.1038/nnano.2011.71

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Metadata

Authors
Eichler, A., Moser, J., Chaste, J., Zdrojek, M., Wilson-Rae, I., & Bachtold, A.
Venue
Nature Nanotechnology
Published
2011-05-15
Volume
6
Issue
6
Pages
339-342
ISSN
  • 1748-3387 (ISSN-L)
  • 1748-3395
OpenAlex
W2115412956
Cited by
669
References
2
FWCI
29.19
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Nonlinear damping in mechanical resonators made from carbon nanotubes and graphene. https://app.cochat.ai/papers/10.1038/nnano.2011.71
The paper
Eichler, A., Moser, J., Chaste, J., Zdrojek, M., Wilson-Rae, I., & Bachtold, A. (2011). Nonlinear damping in mechanical resonators made from carbon nanotubes and graphene. Nature Nanotechnology. https://doi.org/10.1038/nnano.2011.71

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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.