Issue 30, 2026, Issue in Progress

Enzymatic electrochemiluminescence sensor based on a ternary luminol–H2O2-confined nanocatalyst system for sensitive determination of uric acid

Abstract

An enzymatic electrochemiluminescence (ECL) sensor based on a ternary luminol system is developed for highly sensitive detection of uric acid (UA). The platform integrates nanochannel-confined platinum nanoparticle (PtNPs) and nitrogen-doped graphene quantum dot (NGQDs) nanocomposites (PtNPs–NGQDs) as coreaction accelerators, with urate oxidase (Uox) covalently immobilized at the nanochannel entrances. In this design, an amino-functionalized vertically ordered mesoporous silica film (NH2-VMSF) confined the PtNPs–NGQDs within its nanochannels, while Uox is attached to the outer surface. The confined PtNPs–NGQDs exhibit a synergistic catalytic effect, amplifying the ECL signal of the luminol–H2O2 system by 16.1-fold. In the presence of UA, immobilized Uox catalyzes its oxidation to generate H2O2, which is subsequently decomposed by the PtNPs–NGQDs nanocomposite, leading to enhanced ECL emission. Under optimized conditions, the sensor exhibits two linear response ranges for UA (0.01–1 µM and 1–50 µM) and a low detection limit of 8.1 nM (S/N = 3). The sensor also demonstrates good selectivity and stability, enabling reliable UA quantification in complex urine samples. This work provides a simple and versatile strategy for constructing sensitive ECL enzymatic platforms suitable for the detection of metabolites.

Graphical abstract: Enzymatic electrochemiluminescence sensor based on a ternary luminol–H2O2-confined nanocatalyst system for sensitive determination of uric acid

Supplementary files

Article information

Article type
Paper
Submitted
13 Mar 2026
Accepted
05 May 2026
First published
22 May 2026
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2026,16, 27756-27764

Enzymatic electrochemiluminescence sensor based on a ternary luminol–H2O2-confined nanocatalyst system for sensitive determination of uric acid

C. Wei, M. Chen, W. Hu, F. Yan and Y. Cui, RSC Adv., 2026, 16, 27756 DOI: 10.1039/D6RA02118B

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