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.
A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.