Cost-effective quantification of uric acid using niobium oxide and graphene oxide-modified pencil-drawn electrodes on PVC substrates

Abstract

This study introduces a cost-effective approach for quantifying uric acid (UA), the main antioxidant species in human physiology and implicated in inflammatory regulation. Using a PVC substrate and pencil drawing technique, electrodes were fabricated and modified with niobium oxide and graphene oxide via a straightforward “drop casting” method. The nanostructures of the substrate, electrode, and modified electrode were evaluated using SEM images. The synergistic effect between these materials significantly facilitated the uric acid oxidation process with a 400 mV peak potential shift and 45% current increase. The evaluation of the electrode's response to common blood and urine components showed minimal deviation. Among the components tested—ascorbic acid, glucose, nitrate, nitrite, cysteine, urea, creatinine, and ammonium ion—only the ammonium ion exhibited a 10% interference at concentrations commonly found in urine. The sensors showed a good detection limit of 8.7 μmol L−1, with a wide linear range from 8.7 to 2000 μmol L−1 with a correlation factor of 0.9993 for five different sensors. The reproducibility and repeatability of the produced sensors were estimated by the RSD at 4% and 1%, respectively. Synthetic urine samples spiked exhibited reliable analysis, with recovery values within a 5% error margin. This work presents a practical, simple, and affordable sensor platform for rapid and accurate UA quantification.

Graphical abstract: Cost-effective quantification of uric acid using niobium oxide and graphene oxide-modified pencil-drawn electrodes on PVC substrates

Supplementary files

Article information

Article type
Paper
Submitted
19 Jul 2024
Accepted
27 Sep 2024
First published
30 Sep 2024

Anal. Methods, 2024, Advance Article

Cost-effective quantification of uric acid using niobium oxide and graphene oxide-modified pencil-drawn electrodes on PVC substrates

D. P. M. Saraiva, B. Ferreira, L. M. A. Ribeiro, T. R. L. C. Paixão and M. Bertotti, Anal. Methods, 2024, Advance Article , DOI: 10.1039/D4AY01345J

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