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Alignment of Carbon Nanotubes under Low Magnetic Fields through Attachment of Magnetic Nanoparticles

Correa-Duarte, Miguel A., Grzelczak, Marek, Salgueiriño-Maceira, Verónica, Giersig, Michael, Liz-Marzán, Luis M., Farle, Michael, Sierazdki, Karl, Diaz, Rodolfo·2005·The Journal of Physical Chemistry B

Abstract

The alignment of multiwalled carbon nanotubes (MWNTs) has been accomplished through deposition of uniform layers of magnetite/maghemite nanoparticles (diameter = 6-10 nm) and use of an external magnetic field. The coating of CNTs with magnetic nanoparticles was performed by combining the polymer wrapping and layer-by-layer (LbL) assembly techniques. The particle-coated MWNTs are superparamagnetic and can be aligned at room temperature on any substrate by deposition from an aqueous solution in an external field B = 0.2 T. The volume magnetization of the particle coated MWNTs is found to be enhanced by 17% compared to the pure particles in a powder indicating that either the adsorption process onto the CNTs changes the particle magnetization, or the MWNTs carry an intrinsic magnetization due to remaining Ni used as a catalyst for the growth process.

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Metadata

Authors
Correa-Duarte, M. A., Grzelczak, M., Salgueiriño-Maceira, V., Giersig, M., Liz-Marzán, L. M., Farle, M., Sierazdki, K., & Diaz, R.
Venue
The Journal of Physical Chemistry B
Published
2005-09-24
Volume
109
Issue
41
Pages
19060-19063
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
OpenAlex
W2087231851
Cited by
339
References
0
FWCI
9.88
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Alignment of Carbon Nanotubes under Low Magnetic Fields through Attachment of Magnetic Nanoparticles. https://app.cochat.ai/papers/10.1021/jp0544890
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Correa-Duarte, M. A., Grzelczak, M., Salgueiriño-Maceira, V., Giersig, M., Liz-Marzán, L. M., Farle, M., Sierazdki, K., & Diaz, R. (2005). Alignment of Carbon Nanotubes under Low Magnetic Fields through Attachment of Magnetic Nanoparticles. The Journal of Physical Chemistry B. https://doi.org/10.1021/jp0544890

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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.