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Gold-platinum bimetallic nanocluster with enhanced peroxidase-like activity and its integrated agarose hydrogel-based sensing platform for colorimetric analysis of glucose level in serum

Abstract

This work presents a novel and facile strategy for the fabrication of gold-platinum bimetallic nanoclusters (Au-PtNCs) with adjustable Au/Pt molar ratios by one-pot synthetic route. It was unexpectedly found that the prepared Au-PtNCs with optimal Au/Pt molar ratio (1:1) could exhibit greatly enhanced peroxidase-like catalytic activity and chemical stability toward harsh conditions due to synergistic effect of the two atoms, in contrast with pure AuNCs. These prominent advantages enable Au-PtNCs to be capable of sensitive and selective colorimetric detection of glucose by means of a NCs-glucose oxidase (GOx) cascade-catalyzed system using 3,3’,5,5’-tetramethylbenzidine (TMB) as the chromogenic substrate. This assay can be used not only for visual detection of glucose by the naked eyes but for reliable and convenient quantification in the range from 5 to 55 µM with detection limit of 2.4 µM. Importantly, to widen the application of point-of-care testing (POCT) of glucose in biomedical diagnosis, an integrated agarose hydrogel-based sensing platform including NCs, GOx and TMB was rationally designed. It was demonstrated that this sensing platform could serve as a reagentless and instrument-free platform for direct visualization of glucose with different levels in human serum, in which the results were in good accordance with those obtained from free NCs-involved detection system as well as from commercial blood glucometer.

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Publication details

The article was received on 12 Aug 2017, accepted on 11 Sep 2017 and first published on 14 Sep 2017


Article type: Paper
DOI: 10.1039/C7AN01343D
Citation: Analyst, 2017, Accepted Manuscript
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    Gold-platinum bimetallic nanocluster with enhanced peroxidase-like activity and its integrated agarose hydrogel-based sensing platform for colorimetric analysis of glucose level in serum

    J. Feng, P. Huang and F. Wu, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C7AN01343D

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