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Deep Learning-Assisted Room-Temperature Phosphorescence Sensor Array Based on Host-Guest Doping for Visual

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This study developed a simple sensor array for visually distinguishing triazole fungicides (TFs) using room-temperature phosphorescence. The method accurately identifies TF mixtures, offering potential for environmental and food safety monitoring.

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Spectroscopy

Background:

  • Distinguishing structurally similar triazole fungicides (TFs) is crucial for environmental and food safety.
  • Existing methods for TF detection can be challenging and lack visual discrimination capabilities.

Purpose of the Study:

  • To develop a straightforward room-temperature phosphorescence (RTP) sensor array for visual discrimination of TF subtypes.
  • To enable sensitive and accurate detection of TFs in complex matrices.

Main Methods:

  • Doping five TF subtypes into a boric acid (BA) matrix to create RTP-active composites.
  • Utilizing host-guest doping-induced RTP signal amplification for enhanced sensitivity.
  • Employing time-resolved RTP and chemometric analysis (LDA, HCA) for TF discrimination.
  • Developing an artificial vision platform with DenseNet for automated identification.

Main Results:

  • The BA matrix amplified RTP signals, producing multicolored, long-lived afterglow composites.
  • Concentration-dependent RTP fingerprints enabled discrimination of individual TFs and their mixtures.
  • The artificial vision platform achieved >91% accuracy in automated TF identification within 1 second.
  • Robust TF detection was achieved in real samples, free from background interference.

Conclusions:

  • The developed RTP sensor array provides a visual and effective strategy for trace-level TF discrimination.
  • This approach demonstrates significant potential for on-site environmental and food safety monitoring applications.
  • The integration of RTP sensing with artificial intelligence offers a powerful tool for chemical analysis.