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Related Experiment Video

Updated: Jul 15, 2025

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
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A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter

Xiangfen Xue1, Mingjie Wei2, Jing Yuan1

  • 1School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, 212013, PR China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|September 30, 2023
PubMed
Summary

A new virtual sensor array (VSA) uses inner filter effect (IFE) and 3D fluorescence spectroscopy for rapid, low-cost environmental monitoring. This method effectively detects and discriminates aromatic pollutants, even in complex mixtures and water samples.

Keywords:
Inner filter effectLeast-squares methodPrincipal component analysisSupport vector machineThree-dimensional fluorescence spectroscopyVirtual sensor array

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Environmental monitoring demands convenient, fast, and low-cost detection methods for pollutants.
  • Existing methods face technological challenges in discriminating structurally similar compounds.
  • Inner filter effect (IFE) and fluorescence spectroscopy offer potential for sensitive detection.

Purpose of the Study:

  • To develop a virtual sensor array (VSA) for economic and effective recognition of aromatic pollutants.
  • To utilize pattern recognition techniques for discriminating compounds with similar structures.
  • To establish a novel approach for VSA construction using fluorescence spectroscopy and IFE.

Main Methods:

  • Constructed a VSA using a single metal-organic framework (MOF-74 (Zn)) recognition unit.
  • Employed three-dimensional fluorescence spectroscopy with varying excitation wavelengths.
  • Applied principal component analysis (PCA) for data visualization and support vector machine (SVM) for prediction modeling.

Main Results:

  • Achieved 100% classification accuracy for single aromatic analytes and 96% for binary mixtures.
  • Successfully predicted concentrations of phenol, phenylamine, and nitrobenzene within acceptable error ranges.
  • Demonstrated VSA's capability to distinguish pollutants in real water samples (tap and river water).

Conclusions:

  • The developed VSA, based on IFE and 3D fluorescence, provides a powerful tool for environmental pollutant detection.
  • This method offers a generalizable strategy for VSA construction, enabling efficient analysis of complex mixtures.
  • The approach effectively addresses the need for rapid, cost-effective, and accurate environmental monitoring solutions.