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

Area of Science:

• Environmental Chemistry
• Materials Science
• Analytical Chemistry

Background:

• Arsenic speciation is crucial for understanding toxicity and guiding water contamination response.
• Current analytical methods face challenges in rapid and simultaneous identification of multiple arsenic species.

Purpose of the Study:

• To develop a novel machine learning-driven fluorescent sensor array for differentiating four arsenic species.
• To create an intelligent platform for rapid arsenic speciation analysis in water samples.

Main Methods:

• Synthesis of two Fe-based luminescent metal-organic frameworks (NH2-MIL-88(Fe) and OH-MIL-88(Fe)).
• Utilizing differential fluorescence responses of the frameworks to various arsenic species.
• Employing pattern recognition and machine learning algorithms for data analysis and prediction.

Main Results:

• The sensor array successfully differentiated arsenite (AsIII), arsenate (AsV), monomethylarsonic acid (MMAV), and dimethylarsinic acid (DMAV).
• A machine learning algorithm integrated with the sensor array enabled precise identification and prediction of arsenic species, including mixtures.
• The platform demonstrated successful application in analyzing actual water samples.

Conclusions:

• A robust, rapid, and intelligent platform for arsenic speciation was developed.
• The fluorescent sensor array offers a powerful tool for water quality assessment and emergency response.
• Machine learning integration enhances the capability for complex arsenic speciation analysis.