Publications

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A short-standoff bistatic lidar system coupled with an aerosol chamber has been built to measure aerosol optical backscatter and laser induced fluorescence cross-sections. Preliminary results show good sensitivity across all channels with high signal-to-noise ratio. © OSA 2012.

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.

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We demonstrate a pump-probe approach for the detection of organophosphonate compounds on substrates, in which the pump pulse fragments the parent molecule and the released phosphorous monoxide (PO) fragment is probed using laser-induced fluorescence. © 2011 OSA.

We demonstrate a pump-probe approach for the detection of organophosphonate compounds on substrates, in which the pump pulse fragments the parent molecule and the released phosphorous monoxide (PO) fragment is probed using laser-induced fluorescence. © 2011 OSA.

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.

We review three photofragmentation detection approaches, describing the detection of (1) vapor-phase mercuric chloride by photofragment emission, (2) vapor-phase nitro-containing compounds by photofragmentation-ionization, and (3) surface-bound organophosphonate compounds by photofragmentation-laser-induced fluorescence. © 2010 Optical Society of America.

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.

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Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.

The search is on for new renewable energy and algal-derived biofuel is a critical piece in the multi-faceted renewable energy puzzle. It has 30x more oil than any terrestrial oilseed crop, ideal composition for biodiesel, no competition with food crops, can be grown in waste water, and is cleaner than petroleum based fuels. This project discusses these three goals: (1) Conduct fundamental research into the effects that dynamic biotic and abiotic stressors have on algal growth and lipid production - Genomics/Transcriptomics, Bioanalytical spectroscopy/Chemical imaging; (2) Discover spectral signatures for algal health at the benchtop and greenhouse scale - Remote sensing, Bioanalytical spectroscopy; and (3) Develop computational model for algal growth and productivity at the raceway scale - Computational modeling.

Progress in algal biofuels has been limited by significant knowledge gaps in algal biology, particularly as they relate to scale-up. To address this we are investigating how culture composition dynamics (light as well as biotic and abiotic stressors) describe key biochemical indicators of algal health: growth rate, photosynthetic electron transport, and lipid production. Our approach combines traditional algal physiology with genomics, bioanalytical spectroscopy, chemical imaging, remote sensing, and computational modeling to provide an improved fundamental understanding of algal cell biology across multiple cultures scales. This work spans investigations from the single-cell level to ensemble measurements of algal cell cultures at the laboratory benchtop to large greenhouse scale (175 gal). We will discuss the advantages of this novel, multidisciplinary strategy and emphasize the importance of developing an integrated toolkit to provide sensitive, selective methods for detecting early fluctuations in algal health, productivity, and population diversity. Progress in several areas will be summarized including identification of spectroscopic signatures for algal culture composition, stress level, and lipid production enabled by non-invasive spectroscopic monitoring of the photosynthetic and photoprotective pigments at the single-cell and bulk-culture scales. Early experiments compare and contrast the well-studied green algae chlamydomonas with two potential production strains of microalgae, nannochloropsis and dunnaliella, under optimal and stressed conditions. This integrated approach has the potential for broad impact on algal biofuels and bioenergy and several of these opportunities will be discussed.

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 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.

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We demonstrate photofragment emission with a compact fiber-laser source to measure gaseous HgCl2 concentration with the goal of developing a real-time stand-off speciating mercury emissions monitor. Assuming typical flue gas concentrations (74% N2, 12% CO2, 8% H2O, and 6% O2) at 200°C, we calculate a stand-off detection limit of 0.7 ppb for a fieldable monitoring instrument.

A discussion on an active gas imager that can potentially improve system performance and reliability in Smart Leak Detection and Repair covers conventional single-wavelength imaging; differential imaging; methane detection; modification for detecting fugitive emissions relevant to refineries and chemical plants; and system description. This is an abstract of a paper presented at the AWMA's 99th Annual Conference and Exhibition (New Orleans, LA 6/20-23/2006).

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Because many solid objects, both stationary and mobile, will be present in an indoor environment, the design of an indoor aerosol cloud finding lidar (light detection and ranging) instrument presents a number of challenges. The cloud finder must be able to discriminate between these solid objects and aerosol clouds as small as 1-meter in depth in order to probe suspect clouds. While a near IR ({approx}1.5-{micro}m) laser is desirable for eye-safety, aerosol scattering cross sections are significantly lower in the near-IR than at visible or W wavelengths. The receiver must deal with a large dynamic range since the backscatter from solid object will be orders of magnitude larger than for aerosol clouds. Fast electronics with significant noise contributions will be required to obtain the necessary temporal resolution. We have developed a laboratory instrument to detect aerosol clouds in the presence of solid objects. In parallel, we have developed a lidar performance model for performing trade studies. Careful attention was paid to component details so that results obtained in this study could be applied towards the development of a practical instrument. The amplitude and temporal shape of the signal return are analyzed for discrimination of aerosol clouds in an indoor environment. We have assessed the feasibility and performance of candidate approaches for a fieldable instrument. With the near-IR PMT and a 1.5-{micro}m laser source providing 20-{micro}J pulses, we estimate a bio-aerosol detection limit of 3000 particles/l.