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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Perfluoroalkyl substances (PFASs) pose significant environmental and health risks.
  • Conventional methods for PFAS detection are limited, necessitating advanced analytical platforms.
  • Simultaneous quantification of multiple PFASs in complex samples is challenging.

Purpose of the Study:

  • To develop an efficient sensing platform for the simultaneous quantification of multiple PFASs in water.
  • To overcome the limitations of existing methods for PFAS detection.
  • To provide an easy-to-perform analytical solution for complex water samples.

Main Methods:

  • Integration of a fluorescence sensor array with a deep learning algorithm.
  • Utilizing distinct fluorescence quenching effects of different PFAS species on fluorescent dyes.
  • Employing a residual neural network for feature interpretation of 3D fluorescence spectra.

Main Results:

  • Achieved simultaneous and comprehensive quantification of five types of PFASs.
  • Demonstrated effective analysis in complex water samples.
  • Validated the platform's ability to interpret information-rich fluorescence spectra.

Conclusions:

  • The developed platform offers a facile and rapid method for multiple PFAS analysis.
  • This novel strategy expands the methodological boundaries of analytical sensing for environmental contaminants.
  • The approach provides a promising solution for monitoring PFAS contamination in water sources.