Issue 37, 2019

Gold nanoparticle etching induced by an enzymatic-like reaction for the colorimetric detection of hydrogen peroxide and glucose

Abstract

Despite the advantages of colorimetric detection methods, their application to glucose detection in fruit and vegetable samples has been limited by interference from other matrix components. Herein, a colorimetric sensor for hydrogen peroxide and glucose detection was developed based on an enzymatic-like reaction that induced the iodine-mediated etching of gold nanoparticles (AuNPs). The AuNPs were fabricated using a MoO3-nanosheet-assisted photochemical process without any surface modification or purification steps. Furthermore, the MoO3 nanosheet residues and Mo(VI) ions leached during AuNP preparation acted directly as peroxidase-like catalysts to promote the reduction of iodate to iodine in the presence of hydrogen peroxide (or the glucose–glucose oxidase enzymatic system). The generated iodine then etched the AuNPs, resulting in an obvious colour change of the reaction solution that allowed the selective and quantitative spectrophotometric determination of hydrogen peroxide and glucose. The linear ranges for the detection of hydrogen peroxide and glucose were 40–380 μM (R2 = 0.99) and 100–1000 μM (R2 = 0.98), respectively, with detection limits of 0.55 μM and 0.45 μM, respectively. The developed colorimetric method was applied to the determination of glucose in water chestnuts with good recoveries (100–108%), demonstrating its applicability to glucose detection in fruits.

Graphical abstract: Gold nanoparticle etching induced by an enzymatic-like reaction for the colorimetric detection of hydrogen peroxide and glucose

Supplementary files

Article information

Article type
Paper
Submitted
26 Jul 2019
Accepted
21 Aug 2019
First published
21 Aug 2019

Anal. Methods, 2019,11, 4829-4834

Gold nanoparticle etching induced by an enzymatic-like reaction for the colorimetric detection of hydrogen peroxide and glucose

M. Lu, L. Su, Y. Luo, X. Ma, Z. Duan, D. Zhu and Y. Xiong, Anal. Methods, 2019, 11, 4829 DOI: 10.1039/C9AY01599J

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