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February 16, 2026Analytical Chemistry2 citations

Machine Learning-Assisted Detection of Phosgene and Acetyl Chloride via a Dual-Probe Fluorescent Platform with Differential Reactivity

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JDJ. H. DingYMYunhui MengLZLijia Zhang

Key Points

  • The research aims to enhance the detection of phosgene and acetyl chloride using innovative fluorescent probes and machine learning.
  • Designed and synthesized two fluorescent probes, TPA-APPA and TPA-HPO.
  • Evaluated the probes' selectivity and sensitivity towards toxic chemicals.
  • Leveraged machine learning algorithms, such as convolutional neural networks, for signal classification.
  • Applied the probes in bioimaging involving live cells and zebrafish.
  • TPA-APPA showed significant fluorescence enhancement for phosgene and acetyl chloride.
  • The detection limits for both chemicals were notably low.
  • High classification accuracy of the detection strip for toxicant vapors was achieved.

Abstract

Phosgene and acyl chlorides are highly toxic chemicals that pose serious threats to human health and environmental safety, yet their rapid and reliable detection remains a major challenge due to their high reactivity and environmental interferences. Herein, we report the rational design and synthesis of two donor-π-acceptor (D-π-A) fluorescent probes, TPA-APPA and TPA-HPO, which incorporate excited-state intramolecular proton transfer and hybridized localized and charge-transfer characteristics. These probes exhibit fast, highly selective, and sensitive responses toward phosgene, with TPA-APPA showing distinct fluorescence enhancement and colorimetric changes for both phosgene and acetyl chloride at low detection limits. Benefiting from its excellent photostability, TPA-APPA was successfully applied to bioimaging in live cells and zebrafish. Furthermore, by integrating machine learning algorithms, including convolutional neural networks and the Swin Transformer, we developed a ceramic-fiber detection strip capable of intelligent recognition and classification of fluorescence signal variations. This hybrid system achieved high classification accuracy for toxicant vapors, demonstrating the strong potential of coupling advanced fluorescent probes with machine learning for next-generation toxic gas monitoring and bioimaging applications.

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Cite This Study

Ding et al. (2026) studied this question.

synapsesocial.com/papers/6992b3939b75e639e9b084efhttps://doi.org/10.1021/acs.analchem.5c06372
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