Novel electrochemical sensor detects nitrofurantoin in samples, suggesting efficient environmental monitoring techniques.
Nitrofurantoin (NFT) is a commonly used antibiotic for the treatment of bacterial infections. The widespread application of Nitrofurantoin may lead to contamination of the environment. As a result, there is a need to develop efficient detection techniques. This paper presents the development of a novel electrochemical sensor for the highly sensitive determination of Nitrofurantoin. The electrochemical sensor is based on the surface modification of a glassy carbon electrode with Ag decorated FeNi-Metal-Organic framework nanocomposite. A two-step, facile hydrothermal and chemical reduction method used for the successful synthesis of Ag@FeNi-MOF. Various characterization tools were employed to analyze the synthesized nanocomposite material. These included X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Field Emission Scanning Electron Microscopy (FESEM), Raman spectroscopy, Fourier Transform Infrared (FT-IR) spectroscopy, Energy-Dispersive X-ray (EDX) spectroscopy and elemental mapping. Electrochemical tests were performed using cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry analysis. Under optimized conditions, the sensor showed two linear detection ranges of 0.99 μM–20 μM, and 20 μM–555 μM, with a detection limit of 0.490 μM and sensitivity of 6.43 μAμM −1 Cm −2 . The sensor showed excellent reproducibility, repeatability, and selectivity. Moreover, the practicality test of the sensor has been successfully applied to the pharmaceutical drugs and Human Plasma, with excellent recoveries ranging from 101.7% to 104.7%. • Ag-decorated FeNi-MOF nanocomposite used as an electrochemical sensing platform. • High selectivity and repeatability toward nitrofurantoin response. • Nitrofurantoin detected with a detection limit of 0.490 μM. • Two linear ranges obtained: 0.99–20 μM and 20–555 μM. • Successful application in pharmaceutical and human plasma sample.
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Hassanpour et al. (2026) studied this question.
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