Remote assessment of physiological parameters has enabled patient diagnostics without the need for a medical professional to become exposed to potential communicable diseases. In particular, early detection of oxygen saturation, abnormal body temperature, heart rate, and/or blood pressure could affect treatment protocols. The modeling effort in this work uses an adding-doubling radiative transfer model of a seven-layer human skin structure to describe absorption and reflection of incident light within each layer. The model was validated using both abiotic and biotic systems to understand light interactions associated with surfaces consisting of complex topography as well as multiple illumination sources. Using literature-based property values for human skin thickness, absorption, and scattering, an average deviation of 7.7% between model prediction and experimental reflectivity was observed in the wavelength range of 500-1000 nm.
We report on the design, modeling, calibration, and experimental results of a LWIR, spectrally and temporally resolved broad band bi-directional reflectance distribution function measuring device. The system is built using a commercial Fourier transform infrared spectrometer, which presents challenges due to relatively low power output compared to laser based methods. The instrument is designed with a sample area that is oriented normal to gravity, making the device suitable for measuring loose powder materials, liquids, or other samples that can be difficult to measure in a vertical orientation. The team built a radiometric model designed to understand the trade space available for various design choices as well as to predict instrument success at measuring the target materials. The radiometric model was built by using the output of commercial non sequential raytracing tools combined with a scripted simulation of the interferometer. The trade space identified in this analysis will be presented.
The design was based on moving periscopes with custom off axis parabolas to focus the light onto the sample. The system assembly and alignment will be discussed. The calibration method used for the sensor will be detailed, and preliminary measurements from this research sensor will be presented.
This paper describes measurements being made on a series of material systems for the purpose of developing a radiative-transfer model that describes the reflectance of light by granular solids. It is well recognized that the reflectance spectra of granular materials depend on their intrinsic (n(λ) and k(λ)) and extrinsic (morphological) properties. There is, however, a lack of robust and proven models to relate spectra to these parameters. The described work is being conducted in parallel with a modeling effort1 to address this need. Each follows a common developmental spiral in which material properties are varied and the ability of the model to calculate the effects of the changes are tested. The parameters being varied include particle size/shape, packing density, material birefringence, optical thickness, and spectral contribution of a substrate. It is expected that the outcome of this work will be useful in interpreting reflectance data for hyperspectral imaging (HSI), and for a variety of other areas that rely on it.
Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.
Remote detection of a surface-bound chemical relies on the recognition of a pattern, or “signature,” that is distinct from the background. Such signatures are a function of a chemical’s fundamental optical properties, but also depend upon its specific morphology. Importantly, the same chemical can exhibit vastly different signatures depending on the size of particles composing the deposit. We present a parameterized model to account for such morphological effects on surface-deposited chemical signatures. This model leverages computational tools developed within the planetary and atmospheric science communities, beginning with T-matrix and ray-tracing approaches for evaluating the scattering and extinction properties of individual particles based on their size and shape, and the complex refractive index of the material itself. These individual-particle properties then serve as input to the Ambartsumian invariant imbedding solution for the reflectance of a particulate surface composed of these particles. The inputs to the model include parameters associated with a functionalized form of the particle size distribution (PSD) as well as parameters associated with the particle packing density and surface roughness. The model is numerically inverted via Sandia’s Dakota package, optimizing agreement between modeled and measured reflectance spectra, which we demonstrate on data acquired on five size-selected silica powders over the 4-16 μm wavelength range. Agreements between modeled and measured reflectance spectra are assessed, while the optimized PSDs resulting from the spectral fitting are then compared to PSD data acquired from independent particle size measurements.
We describe a photofragment laser-induced fluorescence (PF-LIF) method that can be applied to the short-range-standoff
detection of low-volatility organophosphonate chemical warfare agents (OP-CWAs) on surfaces. It operates by
photofragmenting a surface-bound analyte and then actively interrogating a released phosphorous monoxide (PO)
fragment using LIF. We demonstrate a single-pulse-pair (pump = 500 μJ @ 266 nm; probe = 20 μJ @ 248 nm) surface
detection sensitivity of 30 μg/cm2 for the organophosphonate diisopropyl isothiocyanate phosphonate (DIPP) on
aluminum and 210 μg/cm2 for the same analyte on a more porous concrete surface. By detecting the PO photofragment,
the method indicates the presence of organophosphonates; however, we show that it also responds to other phosphorouscontaining
compounds. Because of its limited specificity, we believe that the method may have most immediate use as a
mapping tool to rapidly identify "hotspots" of OP-CWAs. These would then be confirmed using a more specific tool. As
one method of confirming the presence of OP-CWAs (and identifying the agent), we demonstrate that the probe beam
can be used to acquire Raman-scattering spectra of the target area.
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 = fPT + 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.
Recent EPA regulations targeting mercury (Hg) emissions from utility coal boilers have prompted increased activity in
the development of reliable chemical sensors for monitoring Hg emissions with high sensitivity, high specificity, and
fast time response. We are developing a portable, laser-based instrument for real-time, stand-off detection of Hg
emissions that involves exciting the Hg (6 3P1 ←6 1S0) transition at 253.7 nm and detecting the resulting resonant
emission from Hg (6 3P1). The laser for this approach must be tunable over the Hg absorption line at 253.7 nm, while
system performance modeling has indicated a desired output pulse energy ≥0.1 μJ and linewidth ≤5 GHz (full width at
half-maximum, FWHM). In addition, the laser must have the requisite physical characteristics for use in coal-fired
power plants. To meet these criteria, we are pursing a multistage frequency-conversion scheme involving an optical
parametric amplifier (OPA). The OPA is pumped by the frequency-doubled output of a passively Q-switched,
monolithic Nd:YAG micro-laser operating at 10-Hz repetition rate and is seeded by a 761-nm, cw distributed-feedback
diode laser. The resultant pulse-amplified seed beam is frequency tripled in two nonlinear frequency-conversion steps to
generate 253.7-nm light. The laser system is mounted on a 45.7 cm × 30.5 cm breadboard and can be further condensed
using custom optical mounts. Based on simulations of the nonlinear frequency-conversion processes and current results,
we expect this laser architecture to exceed the desired pulse energy. Moreover, this approach provides a compact, all-solid-
state source of tunable, narrow-linewidth visible and ultraviolet radiation, which is required for many chemical
sensing applications.
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