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Area of Science:

  • Environmental Chemistry
  • Materials Science
  • Analytical Chemistry

Background:

  • Polyfluoroalkyl substances (PFASs) pose significant environmental and health risks.
  • Rapid and accurate multicomponent identification of PFASs is a critical analytical challenge.

Purpose of the Study:

  • To develop a novel sensor array for the rapid, specific screening and discrimination of multiple PFASs.
  • To utilize machine learning for enhanced PFAS identification and quantification.

Main Methods:

  • Fabrication of metal nanocluster (MNC)-immobilized metal-organic framework (MOF) on MOF (ZIF-on-MIL) architectures (M@ZIF-on-MIL).
  • Assembly of a ratio fluorescence sensor array incorporating M@ZIF-on-MIL.
  • Application of machine learning algorithms and statistical analysis for data interpretation.

Main Results:

  • The M@ZIF-on-MIL architecture exhibited enhanced fluorescent properties.
  • PFASs induced M@ZIF-on-MIL dissociation, creating unique ratio fluorescent fingerprints.
  • The sensor array achieved 100% accuracy in identifying and distinguishing eight PFAS species.
  • Accurate detection of individual PFASs and mixtures in various concentrations was demonstrated.

Conclusions:

  • A pioneering ratio fluorescence sensor array for rapid PFAS screening was developed.
  • The method demonstrates high efficacy in detecting PFASs in natural water samples.
  • This work lays the groundwork for applying MNC-immobilized MOF-on-MOF architectures in sensor array technology.