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Cited by 306

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Large-scale production of carbon-coated copper nanoparticles for sensor applications

Open access
Athanassiou, E K, Grass, R N, Stark, W J·2006·Nanotechnology

Abstract

Copper nanoparticles with a mean carbon coating of about 1 nm were continuously produced at up to 10 g h(-1) using a modified flame spray synthesis unit under highly reducing conditions. Raman spectroscopy and solid state (13)C magic angle spinning nuclear magnetic resonance spectroscopy revealed that the thin carbon layer consisted of a sp(2)-hybridized carbon modification in the form of graphene stacks. The carbon layer protected the copper nanoparticles from oxidation in air. Bulk pills of pressed carbon/copper nanoparticles displayed a highly pressure- and temperature-dependent electrical conductivity with sensitivity at least comparable to commercial materials. These properties suggest the use of thin carbon/copper nanocomposites as novel, low-cost sensor materials and offer a metal-based alternative to the currently used brittle oxidic spinels or perovskites.

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Registry

Crossref · 10.1088/0957-4484/17/6/022

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Metadata

Authors
Athanassiou, E. K., Grass, R. N., & Stark, W. J.
Venue
Nanotechnology
Published
2006-02-27
Volume
17
Issue
6
Pages
1668-1673
ISSN
  • 0957-4484 (ISSN-L)
  • 1361-6528
OpenAlex
W2008197106
Cited by
306
References
24
FWCI
8.90
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Large-scale production of carbon-coated copper nanoparticles for sensor applications. https://app.cochat.ai/papers/10.1088/0957-4484/17/6/022
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Athanassiou, E. K., Grass, R. N., & Stark, W. J. (2006). Large-scale production of carbon-coated copper nanoparticles for sensor applications. Nanotechnology. https://doi.org/10.1088/0957-4484/17/6/022

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.