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Machine Learning-Driven Multi-Emission Fluorescence Array for Simultaneous Size Discrimination and Quantification of

Shuqi Li1, Jing Mao1, Chunhui Wu1

  • 1College of Chemistry and Material Science, Sichuan Normal University, Chengdu, Sichuan 610068, China.

Analytical Chemistry
|June 16, 2025
PubMed
Summary

A new fluorescence sensing array with machine learning can simultaneously determine gold nanoparticle size and concentration. This method offers rapid, efficient, and accurate nanomaterial characterization for environmental monitoring.

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Area of Science:

  • Nanotechnology
  • Environmental Science
  • Analytical Chemistry

Background:

  • Gold nanoparticles (AuNPs) have size-dependent environmental behaviors and bioaccumulation risks.
  • Accurate characterization of AuNP dimensions and concentrations is crucial for toxicity assessment.
  • Current analytical methods are often time-consuming, costly, and lack multidimensional analysis capabilities.

Purpose of the Study:

  • To develop a novel multiemission fluorescence sensing array for simultaneous size discrimination and quantification of AuNPs.
  • To integrate quantum dots and machine learning for enhanced nanomaterial analysis.
  • To provide a rapid and efficient alternative to existing analytical platforms.

Main Methods:

  • Development of a fluorescence sensing array using BSA-Eu:ZnS QDs.
  • Utilizing triple emission channels (450/592/616 nm) and the inner filter effect for AuNP detection.
  • Applying machine learning algorithms (ExtraTrees, LDA) for size discrimination and ratiometric analysis for quantification.

Main Results:

  • Simultaneous size discrimination and quantification of AuNPs achieved.
  • Achieved a limit of detection of 0.022 nM for AuNP concentration.
  • Demonstrated 100% classification accuracy for 5-100 nm AuNPs using spectral fingerprints and machine learning.
  • Validated method with spiked environmental waters, showing 90%-104% recovery rates.

Conclusions:

  • The developed sensing strategy enables rapid and efficient characterization of gold nanoparticles.
  • Offers potential for multidimensional data acquisition and enhanced analysis efficiency in environmental monitoring.
  • Provides a robust and accurate method for assessing AuNP size and concentration in complex matrices.