journal article·2020-09Open access

Silver nanoparticles as a bifunctional probe for label-free and reagentless colorimetric hydrogen peroxide chemosensor and cholesterol biosensor

Hoang V. Tran, Tuan V. Nguyen, Lan T.N. Nguyen, Hong S. Hoang, Chinh D. Huynh

Abstract

A colorimetric chemosensor for hydrogen peroxide (H2O2) and biosensor for cholesterol is developed in this work following a ‘label-free and reagentless’ concept. In this strategy, silver nanoparticles (AgNPs) are used as a bifunctional probe, namely, a chemical capture probe for H2O2 recognition and a signal probe for displaying detection results. The working principle of these colorimetric sensors is based on the redox reaction between AgNPs and H2O2. This reaction converts AgNPs into Ag+ ions, which can induce a visible color change from yellow to pinkish or colorless depending on the amount of H2O2. Therefore, H2O2 concentration can be measured. The developed colorimetric H2O2 chemosensor exhibits a highly selective and sensitive property towards H2O2 with a limit of detection (LOD) of 3.5 μM. Subsequently, a colorimetric biosensor is developed for cholesterol detection by combining AgNPs and cholesterol oxidase (ChOx) enzyme. Here, ChOx oxidizes cholesterol into cholest-4-en-3-one and H2O2. Then, the detected as-generated H2O2 concentration reflects the cholesterol level in the sample. This cholesterol biosensor exhibits high selectivity and sensitivity for cholesterol detection with a LOD of 40 μM. The developed sensor is successfully applied to the detection of cholesterol levels in serum samples. Our results show that the AgNP–based colorimetric method is cost-effective, sensitive and selective. It has potential applications in H2O2 and cholesterol detection and in clinical and medical diagnoses.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Nanomaterials in Catalysis · Advanced biosensing and bioanalysis techniques · Electrochemical sensors and biosensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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