Ultrasensitive Toltrazuril Sulfone Detection in Eggs via a AuNPs/NH₂-Ce-MOF-Enhanced Electrochemical Immunosensor

Abstract

The widespread use of toltrazuril (TOL) as a low-toxicity anticoccidial agent in livestock production necessitates sensitive monitoring of its persistent metabolite, toltrazuril sulfone (TOS), which exhibits hepatotoxic and reproductive risks at subchronic exposure levels. Here, we proposed a novel electrochemical immunosensor for the ultrasensitive detection of TOS in eggs, utilizing a decorated amino-functionalized cerium metal-organic framework-gold nanoparticle (NH₂-Ce-MOF/AuNPs) nanocomposite. The NH₂-Ce-MOF was synthesized via a hydrothermal crystallization of (NH₄)₂Ce(NO₃)₆ and 2-aminoterephthalic acid, exhibiting exceptional stability and an expansive surface area. Compared to conventional electrodes, NH₂-Ce-MOF-modified electrodes promoted AuNPs nucleation and provided more active sites for HAuCl₄ deposition, while also enhancing antibody binding, thereby improving the detection sensitivity of electrochemical immunosensors. Subsequently, electrodeposited HAuCl₄ enhanced both electron transfer kinetics (charge transfer resistance decreased by 68% to a bare electrode) and bioreceptor loading capacity. Electrochemical evaluation demonstrated superior sensor performance, achieving a wide linear range of 0.05 ng/mL–100 ng/mL (R²=0.998) with an ultralow limit of detection of 33.2 pg/mL (S/N=3), representing a 3-fold improvement over conventional enzyme-linked immunosorbent assays. The electrochemical immunosensor exhibited excellent selectivity and reproducibility (relative standard deviation<10%). This platform establishes a robust foundation for TOS residues and other hazardous contaminant monitoring in complex food matrices.

Article information

Article type
Paper
Submitted
27 Oct 2025
Accepted
10 Jan 2026
First published
12 Jan 2026

Anal. Methods, 2026, Accepted Manuscript

Ultrasensitive Toltrazuril Sulfone Detection in Eggs via a AuNPs/NH₂-Ce-MOF-Enhanced Electrochemical Immunosensor

J. Li, K. Liu, X. Duan, H. Mo, S. Liu, S. Qin, L. Luo, Y. Wen, Q. Wang and H. Chen, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D5AY01781E

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