Drop-on-demand and inkjet printing technology continues to be a promising method of producing chemical test standards with scalability and flexibility to allow for inexpensive, high-throughput production of samples. This enabling technique provides precise, accurate and highly reproducible test coupons that mimic the hazardous chemicals encountered in various theater scenarios; critical in assessing the performance of existing and future sensors detection capabilities. Under the U.S. Army Forensics Advanced Research Program, the Spectroscopy Branch within the Research and Technology Directorate, DEVCOM CBC, along with internal and external collaborative partners are currently utilizing the Direct Color Systems 1800z flat-bed inkjet printer for deposition of various chemicals on relevant surfaces and GeSiM NP2.1 Nanoplotter for more precise and control droplet deposition to support various optical and non-optical detection objectives. The samples produced under this project are used for the evaluation of trace level energetic materials and illicit drugs of abuse within latent fingerprints, deposition of sorbent polymers onto photonic integrated circuits for vapor detection, point sensors, and more recently exploring enhanced training aids for military working dogs. This work will present results from the characterization of utilized chemical deposition techniques as well as recent experimental results from various assessed detection technologies
Recent work with B. anthracis Delta Sterne spores demonstrated that Raman spectroscopy could be used to discriminate between viable and gamma deactivated spores and provided initial insight into the probable source of discrimination found in the spores. From this previous work, we believe through Raman spectral analyses of viable and deactivated spore samples, significant changes in spectral response can be resolved and ascribed to classes of biomolecules affected by the deactivation processes. We expanded upon this study to include four different Bacillus spores (B. anthracis, B. megaterium, B. thuringiensis, and B. atrophaeus) and probe de-activation techniques to include gamma radiation UV radiation, chemical, and thermal methodologies. We used sequential Raman imaging scanning electron (RISE) microscopy to determine chemical (Raman spectral information) and physical (SEM imaging) variance between viable and deactivated spore samples. Additional use of machine learning algorithms to
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