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Purification strategies and purity visualization techniques for single-walled carbon nanotubes

Park, Tae-Jin, Banerjee, Sarbajit, Hemraj-Benny, Tirandai, Wong, Stanislaus S.·2006·J. Mater. Chem.

Abstract

Traditionally, SWNTs have been prepared by electric arc-discharge, laser ablation, or chemical vapor deposition (CVD) methods, yielding tubes of various diameter and length distributions. However, without exception, as-prepared SWNTs are contaminated with a number of impurities, decreasing the overall yield of usable material. Impurities include transition metal catalysts, such as Fe, Co, and Ni, which are necessary for the growth of SWNTs as well as carbonaceous species such as amorphous carbon, fullerenes, multishell carbon nanocapsules, and nanocrystalline graphite. In this Feature Article, we highlight the role of microscopy in designing and facilitating the interpretation of the success of strategies, including (a) oxidative methods including liquid and gas phase oxidation, (b) chemical functionalization protocols, (c) filtration and chromatography techniques, and (d) microwave heating methods, aimed at rationally purifying single-walled carbon nanotubes.

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Metadata

Authors
Park, T.-J., Banerjee, S., Hemraj-Benny, T., & Wong, S. S.
Venue
J. Mater. Chem.
Published
2006
Volume
16
Issue
2
Pages
141-154
ISSN
  • 0959-9428 (ISSN-L)
  • 1364-5501
OpenAlex
W2137469294
Cited by
226
References
0
FWCI
7.86
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Purification strategies and purity visualization techniques for single-walled carbon nanotubes. https://app.cochat.ai/papers/10.1039/b510858f
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Park, T.-J., Banerjee, S., Hemraj-Benny, T., & Wong, S. S. (2006). Purification strategies and purity visualization techniques for single-walled carbon nanotubes. J. Mater. Chem. https://doi.org/10.1039/b510858f

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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.