Ultraviolet (UV) Raman scattering with a 244-nm laser is evaluated for standoff detection of explosive compounds. The measured Raman scattering albedo is incorporated into a performance model that focused on standoff detection of trace levels of explosives. This model shows that detection at {approx}100 m would likely require tens of seconds, discouraging application at such ranges, and prohibiting search-mode detection, while leaving open the possibility of short-range point-and-stare detection. UV Raman spectra are also acquired for a number of anticipated background surfaces: tile, concrete, aluminum, cloth, and two different car paints (black and silver). While these spectra contained features in the same spectral range as those for TNT, we do not observe any spectra similar to that of TNT.
This project demonstrated the feasibility of a 'pump-probe' optical detection method for standoff sensing of chemicals on surfaces. Such a measurement uses two optical pulses - one to remove the analyte (or a fragment of it) from the surface and the second to sense the removed material. As a particular example, this project targeted photofragmentation laser-induced fluorescence (PF-LIF) to detect of surface deposits of low-volatility chemical warfare agents (LVAs). Feasibility was demonstrated for four agent surrogates on eight realistic surfaces. Its sensitivity was established for measurements on concrete and aluminum. Extrapolations were made to demonstrate relevance to the needs of outside users. Several aspects of the surface PF-LIF physical mechanism were investigated and compared to that of vapor-phase measurements. The use of PF-LIF as a rapid screening tool to 'cue' more specific sensors was recommended. Its sensitivity was compared to that of Raman spectroscopy, which is both a potential 'confirmer' of PF-LIF 'hits' and is also a competing screening technology.
As part of the U.S. Department of Homeland Security Detect-to-Protect program, a multilab [Sandia National Laboratories (SNL), Lawrence Livermore National Laboratories (LLNL), Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), and Los Alamos National Laboratory (LANL)] effort is addressing the need for useable detect-to-warn bioaerosol sensors for public facility protection. Towards this end, the SNL team is employing rapid fluorogenic staining to infer the protein content of bioaerosols. This is being implemented in a flow cytometry platform wherein each particle detected generates coincident signals of forward scatter, side scatter, and fluorescence. Several thousand such coincident signal sets are typically collected to generate a probability distribution over the scattering and fluorescence values. A linear unmixing analysis is performed to differentiate components in the mixture. After forming a library of pure component distributions from measured pure material samples, the distribution of an unknown mixture of particles is treated as a linear combination of the pure component distributions. The scattering/fluorescence probability distribution data vector a is considered the product of two vectors, the fractional profile f and the scattering/ fluorescence distributions from pure components P. A least squares procedure minimizes the magnitude of the residual vector e in the expression a = fP T + e. The profile f designates a weighting fraction for each particle type included in the set of pure components, providing the composition of the unknown mixture. We discuss testing of this analysis approach and steps we have taken to evaluate the effect of interferents, both known and unknown.
As part of the U.S. Department of Homeland Security Detect-to-Protect (DTP) program, a multilab [Sandia National Laboratories (SNL), Lawrence Livermore National Laboratories (LLNL), Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), and Los Alamos National Laboratory (LANL)] effort is addressing the need for useable detect-to-warn bioaerosol sensors for public facility protection. Towards this end, the SNL team is investigating the use of rapid fluorogenic staining to infer the protein content of bioaerosols. This is being implemented in a flow cytometer wherein each particle detected generates coincident signals of correlated forward scatter, side scatter, and fluorescence. Several thousand such coincident signal sets are typically collected to generate a distribution describing the probability of observing a particle with certain scattering and fluorescence values. These data are collected for sample particles in both a stained and unstained state. A linear unmixing analysis is performed to differentiate components in the mixture. In this paper, we discuss the implementation of the staining process and the cytometric measurement, the results of their application to the analysis of known and blind samples, and a potential instrumental implementations that would use staining.