Ag nanoparticles grown on waste eggshell membrane as an efficient monolithic oxidase mimic for ascorbic acid colorimetric detection

Abstract

Nanozymes act as a promising alternative to natural enzymes in constructing colorimetric sensing platform for biosensing and food analysis, yet most nanozymes are in power form and requires special separation prior to colorimetric detection. To address such issue, monolithic architecture has attracted more attention due to its simple operation and convenient control. In this work, Ag nanoparticles (Ag NPs) grown on discarded eggshell membranes (Ag/ESM) was proposed as a novel biomaterial based monolithic oxidase mimic to fabricate colorimetric sensing platform for ascorbic acid (AA) detection. Due to the abundant reductive functional groups in ESM protein, Ag NPs was acquired through the auto-reduction of Ag+ without the participation of other reductive agents. These Ag NPs feature with small size and high dispersion, which make it highly active in triggering 3,3′,5,5′-tetramethylbenzidine (TMB) oxidation, with a low Km of 0.118 mM and a high Vmax of 1.32 × 10-7 M·s-1. More significantly, Ag/ESM as monolithic oxidase mimic holds great merits of simple operation and facile control of the enzyme-mimicking reaction for on-demand analysis. A colorimetric sensing platform based on Ag/ESM-TMB system was established, which exhibits excellent sensitivity and selectively for AA detection, as well as remarkable feasibility and reliability for real food samples. This work exemplifies a novel “turn trash to treasure” strategy to fabricate Ag-based monolithic oxidase mimic by using waste ESM as matrix, and establishes a simple but sensitive colorimetric sensing platform for AA in food samples.

Supplementary files

Article information

Article type
Paper
Submitted
29 May 2026
Accepted
24 Jun 2026
First published
24 Jun 2026

Anal. Methods, 2026, Accepted Manuscript

Ag nanoparticles grown on waste eggshell membrane as an efficient monolithic oxidase mimic for ascorbic acid colorimetric detection

Q. Deng, S. Xiao, J. Xie, Q. Liu, D. He, P. Jiang and D. Yuan, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D6AY01053A

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