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Supercapacitor electrodes from multiwalled carbon nanotubes

Frackowiak, E., Metenier, K., Bertagna, V., Beguin, F.·2000·Applied Physics Letters

Abstract

Electrochemical characteristics of supercapacitors built from multiwalled carbon nanotubes electrodes have been investigated and correlated with microtexture and elemental composition of the materials. Capacitance has been estimated by cyclovoltammetry at different scan rates from 1 to 10 mV/s, galvanostatic discharge, and impedance spectroscopy in the frequency range from 100 kHz to 1 mHz. The presence of mesopores due to the central canal and/or entanglement is at the origin of an easy accessibility of the ions to the electrode/electrolyte interface for charging the electrical double layer. Pure electrostatic attraction of ions as well as quick pseudofaradaic reactions have been detected upon varying surface functionality. The values of specific capacitance varied from 4 to 135 F/g, depending on the type of nanotubes or/and their posttreatments. Even with moderate specific surface area (below 470 m2/g), due to their accessible mesopores, multiwalled carbon nanotubes represent attractive materials for supercapacitors as compared to the best activated carbons.

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Metadata

Authors
Frackowiak, E., Metenier, K., Bertagna, V., & Beguin, F.
Venue
Applied Physics Letters
Published
2000-10-09
Volume
77
Issue
15
Pages
2421-2423
ISSN
  • 0003-6951 (ISSN-L)
  • 1077-3118
OpenAlex
W1989723802
Cited by
722
References
0
FWCI
8.54
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Supercapacitor electrodes from multiwalled carbon nanotubes. https://app.cochat.ai/papers/10.1063/1.1290146
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Frackowiak, E., Metenier, K., Bertagna, V., & Beguin, F. (2000). Supercapacitor electrodes from multiwalled carbon nanotubes. Applied Physics Letters. https://doi.org/10.1063/1.1290146

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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.