Machine learning-assisted ratiometric fluorescent sensing platform based on antenna effect in Tb-doped metal-organic framework for visual quantification of enoxacin.

In this work, a ratiometric fluorescent sensing platform based on the antenna effect in Tb-doped metal-organic framework (Tb-MOF) was constructed for sensitive and visual detection of enoxacin (Eno). The coordination of Tb3+ with the β-diketone structure of the Eno molecule activates yellowish-green fluorescence emission through the "antenna effect". The Tb-MOF fluorescent probe exhibits excellent response-sensitivity and selectivity for Eno with a low detection limit (LOD) of 0.09 μM, showing a good linear response in the concentration range of 0-20 μM. Under excitation with 365 nm ultraviolet light, the fluorescence color of the sensor system gradually changes from blue to yellow-green as the Eno concentration increases. Further, Tb-MOF ratiometric fluorescent sensor combined with cloud-based machine learning algorithm-assisted WeChat Mini Program realized the intelligent quantification of Eno in complex matrices such as honey, milk, and beef was achieved, which overcomes the reliance of traditional methods on large-scale instruments and provides a portable and efficient solution for on-site monitoring of food safety.