Machine learning-assisted ratiometric Zr-MOF-based fluorescent probe for intelligent detection of uranyl ions

Uranium contamination in aquatic environments poses potential threats to ecosystems and human health due to the high radioactive and chemical toxicity of uranyl ions (UO₂^2 +), necessitating the development of accurate and rapid detection strategies for the monitoring of UO₂²⁺. Herein, a zirconium-based metal-organic framework (NH₂-PCN-134) was constructed through a one-pot hydrothermal method and employed as a ratiometric fluorescent probe for the detection of UO₂²⁺. The ratiometric fluorescence response of NH₂-PCN-134 toward UO₂²⁺ featured emission quenching at 655 nm and enhancement at 515 nm, enabling quantitative analysis based on the I₅₁₅/I₆₅₅ intensity ratio. Furthermore, the probe exhibited a broad linear response range of 0-200 μM with a low detection limit of 1.23 μM, and excellent selectivity toward UO₂²⁺ in the presence of various interfering ions. The ratiometric fluorescence response was attributed to the synergistic effects of photoinduced electron transfer (PET) and molecular interactions between NH₂-PCN-134 and UO₂²⁺. Moreover, the color features extracted from fluorescent images were integrated with multiple machine learning models for quantitative prediction, which exhibited strong agreement between the predicted and experimental values even in the presence of interfering ions, demonstrating their effectiveness for quantitative analysis. This study provides a machine learning-assisted ratiometric fluorescence sensing strategy for intelligent detection of UO₂²⁺, highlighting its potential for environmental monitoring.