Issue 15, 2014

Enhanced nonenzymatic sensing of hydrogen peroxide released from living cells based on Fe3O4/self-reduced graphene nanocomposites

Abstract

A novel, sensitive, nonenzymatic amperometric sensor for the reliable detection of extracellular H2O2 released from living cells was fabricated based on Fe3O4/reduced graphene oxide (Fe3O4/rGO) nanocomposites, which were prepared using a simple and cost-effective one-pot approach. Electrochemical performances of the Fe3O4/rGO nanocomposites modified glassy carbon electrode (GCE) were studied. The results demonstrated that this H2O2 sensor exhibited excellent electrocatalytic performance towards the reduction of H2O2 at a potential of −0.3 V in the wide linear concentration range from 0.001 to 20 mM with a high sensitivity of 387.6 μA mM−1 cm−2 and a detection limit as low as 0.17 μM (S/N = 3), which was lower than certain enzymes and noble metal nanomaterials-based biosensors. Moreover, good anti-interference property, reproducibility, and long-term stability of the enzymeless sensor were achieved. Because of these remarkable analytical advantages, a novel, effective approach for the detection of extracellular H2O2 released from HeLa cells stimulated by CdTe quantum dots (QDs) was established by the constructed sensor. Since H2O2 is a byproduct of several oxidative biological reactions, this work could be applied to study the downstream biological effects of various stimuli in pathophysiology, and may expand the application of Fe3O4-based nanomaterials in the field of electrochemical sensing and bioanalysis.

Graphical abstract: Enhanced nonenzymatic sensing of hydrogen peroxide released from living cells based on Fe3O4/self-reduced graphene nanocomposites

Supplementary files

Article information

Article type
Paper
Submitted
05 Mar 2014
Accepted
08 May 2014
First published
09 May 2014

Anal. Methods, 2014,6, 6073-6081

Enhanced nonenzymatic sensing of hydrogen peroxide released from living cells based on Fe3O4/self-reduced graphene nanocomposites

H. Fang, Y. Pan, W. Shan, M. Guo, Z. Nie, Y. Huang and S. Yao, Anal. Methods, 2014, 6, 6073 DOI: 10.1039/C4AY00549J

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