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<p>The rapid detection and selective discrimination of chemical warfare agents (CWAs) remain a critical unmet challenge for field–deployable sensing technologies. Each CWA class (blood, choking, blister, etc.) shares common structural motifs, yet specific identification of individual members is essential for real–time hazard assessment. Conventional techniques such as gas chromatography-mass spectrometry (GC–MS), ion mobility spectrometry (IMS), and Fourier-transform infrared spectroscopy (FT-IR) are precise but rely on bulky instrumentation and trained operators, limiting their use in emergency scenarios. Herein, we report a machine learning–assisted colorimetric/fluorometric sensor array for classifying seven representative CWAs (hydrogen cyanide, cyanogen chloride, phosgene, chloropicrin, sulfur mustard, nitrogen mustards, lewisites). The platform employs 29 commercially available dyes that generate distinct optical fingerprints under visible and UV illumination (λ = 365&#xa0;nm), captured by a smartphone–based imaging system and quantified as CIE Lab coordinates. Systematic statistical optimization (HCA, ANOVA, correlation analysis) reduced redundancy and identified an informative subset, while multivariate methods (LDA, t–SNE) confirmed robust separation across concentration ranges. The optimized array exhibited high accuracy, reproducibility, and portability, underscoring its potential as a cost–effective tool for on–site CWA monitoring.</p>

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A multiplex chromogenic–fluorogenic sensor array for comprehensive optical discrimination of chemical warfare agents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • Soohwan Kim,
  • Jin Yoo,
  • Ku Kang,
  • Jeongyun Kim,
  • Myeongsik Shin,
  • Yeon Kyung Cha,
  • David G. Churchill,
  • Min-Kun Kim,
  • Doo-Hee Lee

摘要

The rapid detection and selective discrimination of chemical warfare agents (CWAs) remain a critical unmet challenge for field–deployable sensing technologies. Each CWA class (blood, choking, blister, etc.) shares common structural motifs, yet specific identification of individual members is essential for real–time hazard assessment. Conventional techniques such as gas chromatography-mass spectrometry (GC–MS), ion mobility spectrometry (IMS), and Fourier-transform infrared spectroscopy (FT-IR) are precise but rely on bulky instrumentation and trained operators, limiting their use in emergency scenarios. Herein, we report a machine learning–assisted colorimetric/fluorometric sensor array for classifying seven representative CWAs (hydrogen cyanide, cyanogen chloride, phosgene, chloropicrin, sulfur mustard, nitrogen mustards, lewisites). The platform employs 29 commercially available dyes that generate distinct optical fingerprints under visible and UV illumination (λ = 365 nm), captured by a smartphone–based imaging system and quantified as CIE Lab coordinates. Systematic statistical optimization (HCA, ANOVA, correlation analysis) reduced redundancy and identified an informative subset, while multivariate methods (LDA, t–SNE) confirmed robust separation across concentration ranges. The optimized array exhibited high accuracy, reproducibility, and portability, underscoring its potential as a cost–effective tool for on–site CWA monitoring.