Differentiating Between V- and G-Series Nerve Agent and Simulant Vapours Using Fluorescent Film Responses

SMALL METHODS(2024)

引用 0|浏览2
暂无评分
摘要
In-field rapid and reliable identification of nerve agents is critical for the protection of Defence and National Security personnel as well as communities. Fluorescence-based detectors can be portable and provide rapid detection of chemical threats. However, most current approaches cannot differentiate between dilute vapors of nerve agent classes and are susceptible to false positives due to the presence of common acids. Here a fluorescence-based method is shown for rapid differentiation between the V-series and phosphonofluoridate G-series nerve agents and avoids false positives due to common acids. Differentiation is achieved through harnessing two different mechanisms. Detection of the V-series is achieved using photoinduced hole transfer whereby the fluorescence of the sensing material is quenched in the presence of the V-series agent. The G-series is detected using a turn-on mechanism in which a silylated excited state intramolecular proton transfer sensing molecule is selectively deprotected by hydrogen fluoride, which is typically found as a contaminant and/or breakdown product in G-series agents such as sarin. The strategy provided discrimination between classes, as the sensor for the G-series agent class is insensitive to the V-series agent, and vice versa, and neither responded to common acids. Fluorescence-based detection of chemical warfare agents has been extensively explored for solution and liquid samples, but progress in non-contact vapor detection has been limited. Here two sensing materials are developed to detect vapors associated with fluorinated G-agents and V-agents using fluorescence "turn-on" and "turn-off" mechanisms, respectively, with the former sensor able to distinguish between G-agents and common acid interferents.image
更多
查看译文
关键词
fluorescence,G-series nerve agents,thin films,vapour detection,V-series nerve agents
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要