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Issue 42, 2018
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2D g-C3N4–MnO2 nanocomposite for sensitive and rapid turn-on fluorescence detection of H2O2 and glucose

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Abstract

The accurate determination and quantification of H2O2 and glucose are necessary for diagnosis and bioengineering. Herein, we have reported a novel method for the rapid and selective detection of H2O2 and glucose using g-C3N4–MnO2 nanocomposite. In this method, the fluorescence of graphitic-C3N4 (g-C3N4) was quenched by MnO2, which was attributed to fluorescence resonance energy transfer (FRET) between g-C3N4 nanosheets and the deposited MnO2. When H2O2 was introduced, the MnO2 was reduced to Mn2+, which led to the elimination of FRET and the recovery of the quenched fluorescence. This H2O2 detection system was also suitable for glucose detection since H2O2 is one of the main products in the oxidation reaction of glucose catalyzed by glucose oxidase. Therefore, our method has a great application prospect in diabetes research and clinical diagnosis. Additionally, the fluorescence was linear with the concentration of H2O2 in the range of 0 to 130 μM, with the detection limit of 1.5 μM, and also showed a good linear relationship with glucose in the range of 0 to 150 μM, with the detection limit of 1.5 μM. In addition to a wide linear response, the proposed sensor showed advantageous characteristics, such as easy preparation, low cost, high selectivity, rapid detection, and turn-on fluorescence response. Therefore, our proposed strategy would provide a new general strategy to develop low-cost, sensitive biological and clinical diagnostic methods.

Graphical abstract: 2D g-C3N4–MnO2 nanocomposite for sensitive and rapid turn-on fluorescence detection of H2O2 and glucose

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Publication details

The article was received on 03 Sep 2018, accepted on 02 Oct 2018 and first published on 02 Oct 2018


Article type: Paper
DOI: 10.1039/C8AY01932K
Citation: Anal. Methods, 2018,10, 5084-5090
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    2D g-C3N4–MnO2 nanocomposite for sensitive and rapid turn-on fluorescence detection of H2O2 and glucose

    Y. Zhou, L. Li, Y. Wan, T. Chen and X. Chu, Anal. Methods, 2018, 10, 5084
    DOI: 10.1039/C8AY01932K

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