UOx@HMnO2 Biozyme-Nanozyme Driven Electrochemical Platform for Specific Uric Acid Bioassays

Abstract

Uric acid (UA) is a key end product of purine metabolism in the human body, and its abnormal level is associated with many diseases, so accurate monitoring is essential. Existing detection methods have many limitations, such as chromatography, which is cumbersome, time-consuming, and not cost-effective, and serum uric acid analysis, which requires specialized equipment and venous blood collection. In the field of uric acid sensors, electrochemical detection is commonly used but prone to interference, and nanomaterials are improved but complicated to modify. To better block the interference via an easily-made nanocomposites involved system, in this study, MnO2 with peroxidase-mimicking activity was used as a protective shell to encapsulate natural uric acid oxidase (UOx), realizing good combination of nanozymes and biocatalysts. The UOx can selectively catalyze UA and generate H2O2, and the MnO2 nanozymes can make up for the insufficiency of UOx, and the two main components synergistically make the activity of UOx@HMnO2 ultra-high, which can offer simple and versatile methods to prepare highly efficient hybrid biocatalysts for the fields of biosensors and biocatalysis. This provides a simple and general method for the preparation of efficient hybridized biocatalysts in the fields of biosensors and biocatalysis. The detection limit of the fabricated uric acid sensor is as low as 0.74 μM, and the concentration of the actual sample is consistent with that of mass spectrometry, which provides a means of non-invasive detection of uric acid with high sensitivity, high specificity and good accuracy.

Supplementary files

Article information

Article type
Paper
Submitted
05 Dec 2024
Accepted
20 Feb 2025
First published
21 Feb 2025
This article is Open Access
Creative Commons BY-NC license

Analyst, 2025, Accepted Manuscript

UOx@HMnO2 Biozyme-Nanozyme Driven Electrochemical Platform for Specific Uric Acid Bioassays

C. He, H. Liu, M. Yin, J. Chen, W. Huang, H. Zhou, S. Wu and Y. Wang, Analyst, 2025, Accepted Manuscript , DOI: 10.1039/D4AN01512F

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