Designing a Metal Nanocluster‐Based Fluorescence Assay for the Differential Detection of Nitroimidazole and Nitrofuran Antibiotics In Environmental and Food Samples

Amino acid-capped metal nanoclusters (NCs) offer a smart, turn-off sensing platform for nitro-antibiotics via the inner filter effect. The precisely tailored spectral overlap allows unambiguous differentiation between nitro-imidazole and nitro-furans with nanomolar sensitivity and a distinct visual readout. The robust performance of these NCs is further validated in complex real-world matrices, including cow milk and groundwater.

The extensive use and improper disposal of nitro-antibiotics in veterinary medicine pose significant environmental and health risks, necessitating sensitive and selective detection methods. Furthermore, distinguishing between nitroimidazoles and nitrofurans remains challenging. Here, an amino acid-scaffolded metal nanocluster-based differential nitro-antibiotic detection strategy leveraging the inner filter effect (IFE) is presented. Nanoclusters are engineered to align with the distinct absorption maxima of nitroimidazoles (λ _abs ^max = 320 nm) and nitrofurans (λ _abs ^max = 370 nm). L-tyrosine-capped silver nanoclusters (Tyr-Ag NCs) (excitation/emission: 320/410 nm) showed significant photoluminescence (PL) quenching in response to both nitro-antibiotics classes, enabling a turn-off-based detection method. In contrast, L-tryptophan- and L-cysteine-capped copper nanoclusters (Trp-Cu and Cys-Cu NCs), with excitation/emission around 380/500 nm, overlapped spectrally only with nitrofurans, enabling selective quenching and simple visual detection without instrumentation. All three NCs demonstrated nanomolar sensitivity, high selectivity, and minimal interference from non-target species, with their detection mechanisms elucidated in detail. The practicality of the assay is validated through the successful detection of nitro-antibiotics in cow milk and groundwater, demonstrating its reliability in real-world samples. Overall, this study establishes a strategic sensing platform that intentionally leverages the IFE—traditionally considered an experimental artifact—as a powerful and selective tool for antibiotic detection.

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