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Cited by 235

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Facile Preparation of Graphene-Copper Nanoparticle Composite by in Situ Chemical Reduction for Electrochemical Sensing of Carbohydrates

Chen, Qiwen, Zhang, Luyan, Chen, Gang·2012·Analytical Chemistry

Abstract

A novel graphene-copper nanoparticle composite was prepared by the in situ chemical reduction of a mixture containing graphene oxide and copper(II) ions using potassium borohydride as a reductant. It was mixed with paraffin oil and packed into one end of a fused capillary to fabricate microdisc electrodes for sensing carbohydrates. The morphology and structure of the graphene-copper nanoparticle composite were investigated by scanning electron microscopy, X-ray diffraction, and Fourier transform-infrared spectroscopy. The results indicated that copper nanoparticles with an average diameter of 20.8 nm were successfully deposited on graphene nanosheets to form a well interconnected hybrid network. The analytical performance of these unique graphene-copper nanoparticle composite paste electrodes was demonstrated by sensing five carbohydrates in combination with cyclic voltammetry and capillary electrophoresis (CE). The advantages of the composite detectors include higher sensitivity, satisfactory stability, surface renewability, bulk modification, and low expense of fabrication. They should find applications in microchip CE, flowing-injection analysis, and other microfluidic analysis systems.

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Metadata

Authors
Chen, Q., Zhang, L., & Chen, G.
Venue
Analytical Chemistry
Published
2011-12-05
Volume
84
Issue
1
Pages
171-178
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W1969548914
Cited by
235
References
1
FWCI
15.15
Type
journal article
Language
en
Open access
closed

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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.