Issue 22, 2024

A metal–phenolic coordination framework nanozyme exhibits dual enzyme mimicking activity and its application is effective for colorimetric detection of biomolecules

Abstract

Biomolecules play vital roles in many biological processes and diseases, making their identification crucial. Herein, we present a colorimetric sensing method for detecting biomolecules like cysteine (Cys), homocysteine (Hcy), and glutathione (GSH). This approach is based on a reaction system whereby colorless 3,3′,5,5′-tetramethylbenzidine (TMB) undergoes catalytic oxidation to form blue-colored oxidized TMB (ox-TMB) in the presence of hydrogen peroxide (H2O2), utilizing the peroxidase and catalase-mimicking activities of metal–phenolic coordination frameworks (MPNs) of Cu–TA, Co–TA, and Fe–TA nanospheres. The Fe–TA nanospheres demonstrated superior activity, more active sites and enhanced electron transport. Under optimal conditions, the Fe–TA nanospheres were used for the detection of biomolecules. When present, biomolecules inhibit the reaction between TMB and H2O2, causing various colorimetric responses at low detection limits of 0.382, 0.776 and 0.750 μM for Cys, Hcy and GSH. Furthermore, it was successfully applied to real water samples with good recovery results. The developed sensor not only offers a rapid, portable, and user-friendly technique for multi-target analysis of biomolecules at low concentrations but also expands the potential uses of MPNs for other targets in the environmental field.

Graphical abstract: A metal–phenolic coordination framework nanozyme exhibits dual enzyme mimicking activity and its application is effective for colorimetric detection of biomolecules

Supplementary files

Article information

Article type
Paper
Submitted
16 Apr 2024
Accepted
10 May 2024
First published
11 May 2024

Anal. Methods, 2024,16, 3530-3538

A metal–phenolic coordination framework nanozyme exhibits dual enzyme mimicking activity and its application is effective for colorimetric detection of biomolecules

A. E. Chigozie, A. Ravikumar, X. Yang, G. Tamilselvan, Y. Deng, A. Arunjegan, X. Li, Z. Hu and Z. Zhang, Anal. Methods, 2024, 16, 3530 DOI: 10.1039/D4AY00689E

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