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Issue 9, 2016
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Carbon quantum dots directly generated from electrochemical oxidation of graphite electrodes in alkaline alcohols and the applications for specific ferric ion detection and cell imaging

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Abstract

Carbon quantum dots (CQDs) are attracting tremendous interest owing to their low toxicity, water dispersibility, biocompatibility, optical properties and wide applicability. Herein, CQDs with an average diameter of (4.0 ± 0.2) nm and high crystallinity were produced simply from the electrochemical oxidation of a graphite electrode in alkaline alcohols. The as-formed CQDs dispersion was colourless but the dispersion gradually changed to bright yellow when stored in ambient conditions. Based on UV-Vis absorption, fluorescence spectroscopy, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and high-resolution transmission electron microscopy (HRTEM), this colour change appeared to be due to oxygenation of surface species over time. Furthermore, the CQDs were used in specific and sensitive detection of ferric ion (Fe3+) with broad linear ranges of 10–200 μM with a low limit of detection of 1.8 μM (S/N = 3). The application of the CQDs for Fe3+ detection in tap water was demonstrated and the possible mechanism was also discussed. Finally, based on their good characteristics of low cytotoxicity and excellent biocompatibility, the CQDs were successfully applied to cell imaging.

Graphical abstract: Carbon quantum dots directly generated from electrochemical oxidation of graphite electrodes in alkaline alcohols and the applications for specific ferric ion detection and cell imaging

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Publication details

The article was received on 27 Oct 2015, accepted on 09 Feb 2016 and first published on 09 Feb 2016


Article type: Paper
DOI: 10.1039/C5AN02231B
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Citation: Analyst, 2016,141, 2657-2664
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    Carbon quantum dots directly generated from electrochemical oxidation of graphite electrodes in alkaline alcohols and the applications for specific ferric ion detection and cell imaging

    M. Liu, Y. Xu, F. Niu, J. J. Gooding and J. Liu, Analyst, 2016, 141, 2657
    DOI: 10.1039/C5AN02231B

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