Enabling Emergency Response to Arsenic Contamination: Simultaneous and Rapid Identification of Arsenic Speciation by a Machine Learning-Driven Fluorescent Sensor Array

作者：Dali Wei, Yunxiang Fan, Bohan Wu, Yuxuan Shen, Chunmeng Deng, Shen Qiu, Kun Zeng, Ligang Hu, Jingfu Liu, Zhugen Yang, Zhen Zhang · 发表于：Environmental Science & Technology · 年份：2025 · DOI：10.1021/acs.est.5c08536 · 被引用次数：7 · 研究领域：Arsenic contamination and mitigation、Molecular Sensors and Ion Detection、Metal-Organic Frameworks: Synthesis and Applications

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 (NH 2 -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 NH 2 -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.