Deep-Learning-Assisted Fluorescence Sensor Array for Quantitative Screening of Perfluoroalkyl Substances in Water

Key findings from this study

This research indicates that:

• Differential fluorescence quenching patterns enable simultaneous quantification of five PFAS types in single measurements.
• Deep learning feature extraction from three-dimensional spectra provides quantitative discrimination among distinct PFAS species.
• The platform combines rapid operation and facile execution for multiplex PFAS analysis in complex water samples.

Overview

A fluorescence sensor array integrated with deep learning algorithms enables simultaneous quantification of five perfluoroalkyl substance (PFAS) types in complex water samples. The platform exploits differential fluorescence quenching patterns induced by distinct PFAS species on array elements. A residual neural network processes three-dimensional fluorescence spectra to extract quantitative information from the sensor response patterns.

Methods and approach

The sensing platform combines a fluorescence sensor array with a residual neural network algorithm. Individual fluorescent dyes within the array exhibit selective quenching responses to different PFAS species. The system analyzes feature-rich three-dimensional fluorescence spectra through deep learning to achieve multi-target quantification in water matrices.

Results

The integrated platform achieved simultaneous quantification of five PFAS types in complex water samples through deep learning-assisted feature interpretation of fluorescence spectral data. The approach demonstrates rapid analysis capability with facile operational requirements. The system leverages the information content of three-dimensional fluorescence spectra to discriminate and quantify multiple PFAS targets from a single measurement.

Implications

This work addresses a significant analytical gap in environmental monitoring and public health assessment of PFAS contamination. Simultaneous quantification of multiple PFAS targets streamlines screening protocols for complex water matrices, potentially enhancing detection capabilities across environmental and drinking water applications. The integration of sensor array technology with neural network algorithms establishes a generalizable framework applicable to other multiplex sensing scenarios beyond PFAS analysis.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.