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12 May 2016 Photoacoustic spectroscopy for trace vapor detection and standoff detection of explosives
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The Army is investigating several spectroscopic techniques (e.g., infrared spectroscopy) that could allow for an adaptable sensor platform. Current sensor technologies, although reasonably sized, are geared to more classical chemical threats, and the ability to expand their capabilities to a broader range of emerging threats is uncertain. Recently, photoacoustic spectroscopy (PAS), employed in a sensor format, has shown enormous potential to address these ever-changing threats. PAS is one of the more flexible IR spectroscopy variants, and that flexibility allows for the construction of sensors that are designed for specific tasks. PAS is well suited for trace detection of gaseous and condensed media. Recent research has employed quantum cascade lasers (QCLs) in combination with MEMS-scale photoacoustic cell designs. The continuous tuning capability of QCLs over a broad wavelength range in the mid-infrared spectral region greatly expands the number of compounds that can be identified. We will discuss our continuing evaluation of QCL technology as it matures in relation to our ultimate goal of a universal compact chemical sensor platform. Finally, expanding on our previously reported photoacoustic detection of condensed phase samples, we are investigating standoff photoacoustic chemical detection of these materials. We will discuss the evaluation of a PAS sensor that has been designed around increasing operator safety during detection and identification of explosive materials by performing sensing operations at a standoff distance. We investigate a standoff variant of PAS based upon an interferometric sensor by examining the characteristic absorption spectra of explosive hazards collected at 1 m.
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Ellen L. Holthoff, Logan S. Marcus, and Paul M. Pellegrino "Photoacoustic spectroscopy for trace vapor detection and standoff detection of explosives", Proc. SPIE 9824, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVII, 98240R (12 May 2016);

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