Rapid analysis of clandestine laboratories is necessary to maintain a protective posture while gaining an understanding of surroundings. We have shown that there is a potential of using a threat anomaly detection (ThreAD) algorithm that allows for rapid, real-time hyperspectral analysis. Understanding if there are primary reagents or products of explosive materials is of concern to recognize what the potential threat in a clandestine laboratory may be. Herein, we discuss the use of a commercial hyperspectral system that is used to collect data that is analyzed using our ThreAD algorithm for existing and emerging explosive threats. In this work, we look at pure spectra of the principle synthetic components of potential explosive threats and the resulting explosive that is made in a method that is consistent with what may be done in a clandestine laboratory. The spectra are parameterized consistent with what is used in the ThreAD algorithm and is clustered in three-dimensional space. The separation of the principle synthetic components and resulting explosives are compared and related to other explosive threats and potential surfaces that the material may reside on. This provides us with the basis of understanding what threats may be detected as anomalies with our ThreAD algorithm and how they compare to others.
Active matter, such as Janus micromotors have been used for applications such as self-assembly, pollution mitigation, and drug delivery. Metal-Organic Framework (MOF)-based Janus micromotors have been recently explored as a method to increase the rate of decontamination for chemical warfare agents in solution due to favorable MOF-chemical interactions. To achieve active-matter decontamination, SiO2@UiO66@Ag MOF-based Janus micromotors were synthesized. In addition to decontamination, the MOF-based micromotors have favorable surface topography for maintaining a localized surface plasmon. This work explores the plasmonic capabilities of Ag@MOF Janus micromotors by systematically changing the amount of Ag, the size of the microparticle that is being used for the plasmonic sensing, and the underlying MOF structure. By changing these parameters, MOF-based micromotors may be able to be used as sensors by utilizing techniques such as Surface Enhanced Raman Spectroscopy (SERS).
The accidental release of industrial and agricultural chemicals can pose a serious threat to life and the environment. Therefore, researchers have been exploring detection methods of commonly transported chemicals in order to minimize potential harm or destruction in response to an accidental release. One method is to use a network of commercial sensors to track a chemical spill but with each sensor costing upwards of $600, this type of network can become prohibitively expensive and may not be practical for real world use. Specifically, we aimed to develop a network of custom electrical conductivity sensors with each sensor made from an inexpensive Arduino board showing comparable detection results while costing an order of magnitude less. In our experiments, the network of sensors covered 83 in2 in a container filled with different types of water (e.g. deionized, melted snow, sea, river, and tap). The network of custom sensors showed high ammonia concentrations near the release point of an initial laboratory scale ammonia release with low ammonia concentrations away from the release point. As equilibrium was reached, the sensors showed the same ammonia reading. Additionally, a 2-D map was made to track the simulated ammonia spill overtime. Overall, this works shows that this network of custom Arduino sensors can be used to map the detection of accidental ammonia release as an inexpensive replacement for the commercial sensors, which will promote accessibility of future testing for the broader community.
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