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Machine learning-enhanced colorimetric sensor array for rapid detection of nerve agents.

Jeongyun Kim1, Ku Kang2, Myeongsik Shin2

  • 1Department of Chemical and Biological Engineering, Seoul National University, 08826, Seoul, Republic of Korea.

Journal of Hazardous Materials
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning colorimetric sensor array rapidly and accurately identifies authentic nerve agents. This simple, low-cost system offers a vital tool for real-time chemical threat detection and public safety.

Keywords:
Chemical threat monitoringColorimetric sensor arrayFluorescent dyeNerve agent detection

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Area of Science:

  • Chemical sensing
  • Analytical chemistry
  • Machine learning applications

Background:

  • Accurate detection of nerve agents is crucial for security.
  • Existing technologies face limitations in speed, portability, and validation.
  • Need for advanced, real-world applicable detection systems.

Purpose of the Study:

  • To develop a machine learning-enhanced colorimetric sensor array for rapid nerve agent discrimination.
  • To overcome limitations of current detection methods.
  • To create a portable, low-cost, and scalable detection platform.

Main Methods:

  • Data-driven selection of six fluorescent dyes using clustering and statistical analysis.
  • Evaluation against five authentic nerve agents (GA, GB, GD, GF, VX) and a simulant.
  • Dual-mode (Visible/UV) illumination and colorimetric response quantification (RGB-to-ΔE00).
  • Linear discriminant analysis for classification.

Main Results:

  • 100% classification accuracy achieved for authentic nerve agents.
  • Instantaneous visual response with discrimination capability down to 10 μM.
  • Outperformed visible-light-only detection (87.5% accuracy).

Conclusions:

  • The developed sensor array provides a simple, low-cost, and scalable solution.
  • Enables rapid and accurate discrimination of multiple authentic nerve agents.
  • Paves the way for next-generation portable technologies for chemical threat surveillance.