AGC-balanced triple-fluorescence matrix of N-GQDs@Al-MOF nanoparticles for fingerprint identification of nitrofuran antibiotics in milk and farm-related environmental samples.

Overuse of nitrofurans (NFs) can leave residues in livestock-derived foods, posing food-chain risks. Herein, we construct a triple-fluorescence matrix by synthesizing N-GQDs@Al-MOF nanoparticles, dynamically balance each fluorescence contribution on flexibly-fabricated sensor module, and present NFs fingerprint recognition. Morphological and experimental studies are performed on hydrothermally-synthesized N-GQDs@Al-MOF. Results prove NFs-conducted fluorescence quenching, to F430, F470, and F660, are respectively dominated by inner filter effect, static quenching, and charge-transfer-type interactions. Individual dynamic range or resolution from triple-fluorescence signals is rescaled on the homemade module, using automatic gain control (AGC) strategy supported by variable-gain amplifier and self-calibration sequence. The optimized matrix resists material-gap interference, enabling pure principal component analysis data for NFs fingerprinting. Feasible analytical behaviors, including broad dynamic ranges (0.05-50/0.20-20 μg mL-1), good limit of detections (0.046/0.112 μg mL-1), and credible stabilities, are validated on F470/F430-F660. This method reliably identifies single-, dual-, or triple-NFs from complex backgrounds of laboratory and natural samples.