Ru-Au Nanocluster-Functionalized Carbon Fiber Microelectrode Enables Ultrasensitive Multiplexed Monitoring of Acetaminophen and Its Genotoxic Interplay with DNA

Abstract

This study presents the development of a highly sensitive and selective electrochemical sensor based on ruthenium-gold nanocluster-functionalized carbon fiber microelectrodes (AuNPs/RuNPs/CFME) for the ultrasensitive detection of acetaminophen (ACOP) and its genotoxic impurities, p-aminophenol (PAP) and p-nitrophenol (PNP). Leveraging the synergistic electrocatalytic effects of bimetallic nanoparticles, the modified electrode demonstrated exceptional performance, achieving wide linear ranges (0.04-100 μmol L-1 for ACOP, 1-10 μmol L-1 for PAP and PNP) and remarkably low detection limits (6.192 nmol L-1 for ACOP, 0.19 μmol L-1 for PAP, and 0.015 μmol L-1 for PNP). The sensor exhibited outstanding stability, anti-interference capability, and successful application in human serum samples with recoveries of 95.9-100.5%. Furthermore, the study explored the genotoxic interaction between ACOP and double-stranded DNA (dsDNA) using electroanalytical, UV spectrophotometric, and molecular docking techniques. Results revealed that ACOP intercalates into dsDNA via hydrogen bonding and π-π stacking, providing mechanistic insights into its hepatotoxicity at high concentrations. The proposed sensor not only offers a robust platform for multiplexed drug monitoring but also advances understanding of ACOP-induced DNA damage, highlighting its potential in pharmacokinetic studies and toxicity assessment. This work underscores the significance of nanomaterial-enhanced electrochemical sensors for pharmaceutical quality control and toxicity assessment.

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Article information

Article type
Paper
Submitted
14 Jan 2026
Accepted
22 Mar 2026
First published
23 Mar 2026

Anal. Methods, 2026, Accepted Manuscript

Ru-Au Nanocluster-Functionalized Carbon Fiber Microelectrode Enables Ultrasensitive Multiplexed Monitoring of Acetaminophen and Its Genotoxic Interplay with DNA

T. Fang, M. Shi, U. Ishimov, R. Tian, J. Li, H. Cheng, A. Ihsan and X. Huang, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D6AY00071A

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