ArticleScientific reports2025
Electrochemical determination of uric acid in the presence of dopamine and riboflavin using a poly(resorcinol)-modified carbon nanotube sensor.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Smart contact lens-trained digital twin for device-free personalized uric acid prediction.Science advances · 2026Article
- Non-enzymatic dual-mode plasmonic framework for robust bioanalyte detection.Scientific reports · 2026Article
- Carbon nanotube integrated MOF-derived ZnCoRSC advances · 2026Article
- Synthesis of ZnO/SnOJournal of fluorescence · 2026Article
- NiO/CuO@Graphene oxide-modified electrode for sensitive detection of an antidiabetic drug.Scientific reports · 2026Article
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4 authors.
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Abstract
In this study, an electrochemical sensor based on a poly(resorcinol) modified carbon nanotube paste electrode (P(RS)/MCNTPE) was successfully developed for the sensitive and selective detection of uric acid (UA) in the presence of dopamine (DA) and riboflavin (RFN). The sensor shows excellent performance in a 0.2 M phosphate buffer solution (PBS) at pH 7.0 with a scan rate 0.1 V/s. Various electrochemical methods were studied including cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Morphological analysis using scanning electron microscopy (SEM) conformed the enhanced surface properties of the bare carbon nanotubes paste electrode (BCNTPE) and the P(RS)/MCNTPE. The effect of pH, scan rate changes 0.025 to 0.25 V/s, revealed that the oxidation of UA follows an adsorption-controlled process. For UA concentration changes from 20 µM and 380 µM, sensor exhibited a limit of detection (LOD) of 0.18 µM and a limit of quantification (LOQ) of 0.61 µM. Optimal UA response was observed at pH 7.0. The sensor shows good stability, repeatability, and reproducibility. Its analytical applicability was successfully validated in real sample analysis.
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