Enabling Emergency Response to Arsenic Contamination: Simultaneous and Rapid Identification of Arsenic Speciation by...

The rapid identification of arsenic speciation is critical for assessing its toxicity and guiding emergency response during water contamination events, yet it remains a significant challenge for current analytical methods. Herein, a novel machine learning-driven fluorescent sensor array was designed for the differentiation of four arsenic species, including arsenite (As^III), arsenate (As^V), monomethylarsonic acid (MMA^V), and dimethylarsinic acid (DMA^V). Two Fe-based luminescent metal-organic frameworks (NH₂-MIL-88(Fe) and OH-MIL-88(Fe)) were synthesized by functionalizing MIL-88 (Fe) with 2-amino-terephthalic acid and 2-hydroxy-terephthalic acid, respectively, both of which presented promising fluorescence behavior. Remarkably, varying arsenic species differentially regulated the fluorescence intensity of NH₂-MIL-88(Fe) and OH-MIL-88(Fe), which was further analyzed by pattern recognition methods to develop a fluorescence sensor array for the rapid, simultaneous identification of four arsenic species and their mixtures. Furthermore, a machine learning algorithm was employed to integrate with the fluorescent sensor array to establish a stepwise prediction model to precisely identify and predict four arsenic species, which was successfully applied to actual water samples. Thus, our findings presented a robust, rapid, and intelligent platform for arsenic speciation, offering a powerful tool for water quality assessment and emergency response.