Machine learning-enhanced colorimetric sensor array for rapid detection of nerve agents
Jeongyun Kim, Ku Kang, Myeongsik Shin, Soohwan Kim, Jin Yoo, Min‐Kun Kim, Won Bo Lee, Doo-Hee Lee
Abstract
Rapid and reliable discrimination of authentic nerve agents in complex environments is a critical, unsolved challenge for security and public safety. Current detection technologies are often limited by bulky instrumentation, prolonged analysis times, or validation restricted to simulants, hindering their real-world threat-monitoring capabilities. Here, we demonstrate a machine learning-enhanced colorimetric sensor array that overcomes these limitations. We employed a systematic data-driven approach using hierarchical clustering, analysis of variance, and correlation analysis to optimally select just six commercially available fluorescent dyes from an initial 29-candidate library. This optimized array was rigorously evaluated against five authentic nerve agents (GA, GB, GD, GF, VX) and a key simulant using a novel dual-mode (Visible/UV) illumination strategy. By quantifying colorimetric responses (RGB-to- Δ E 00 ), our linear discriminant analysis classifier achieved 100% classification accuracy. The sensor provides an instantaneous visual response and maintains discrimination capability down to 10 μ M, markedly outperforming the 87.5% accuracy of visible-light-only detection. This study establishes a simple, low-cost, and scalable platform for the rapid, accurate discrimination of multiple authentic nerve agents. By integrating data-driven sensor design with robust visual analysis, our system provides a clear pathway toward next-generation portable technologies for real-time chemical threat surveillance. Environmental Implications This study presents a machine learning-enhanced colorimetric sensor array for rapid and accurate nerve agent detection, offering significant potential to mitigate environmental risks from chemical threats. By enabling quick identification of toxic agents, the sensor system can help reduce hazards from chemical warfare agents. The platform is potentially deployable in the field for real-time monitoring, leading to faster response times and reduced contamination risks to ecosystems and humans. Additionally, the use of commercially available, low-cost reagents improves scalability, making the technology a sustainable solution for environmental monitoring and security efforts.