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Infrared Optical Properties of Liquids on Surfaces


OBJECTIVE: To develop a quantitatively accurate, physics-based model for predicting and interpreting infrared (IR) reflectance and emittance spectra of surfaces contaminated with liquids. Emphasis will be on modeling the reflectivity of irregular surfaces and surfaces composed of granular materials in the long wavelength infrared (LWIR, 800 to 1200 wavenumber) spectral region. DESCRIPTION: Detection of liquid contaminants on surfaces is a high priority within the Joint Service Chemical and Biological Defense community. New methods are required to protect DOD personnel from exposure to persistent chemical agents by providing early warning and mapping of contaminated areas. Standoff detection methods based on IR spectroscopy using both IR reflectance and emittance have shown promise in providing wide-area surveillance for contaminated areas; however, IR reflectance and emittance spectra are complex and not easy to analyze. A new suite of physics-based modeling tools will facilitate the development of the next generation of standoff sensor for detecting and mapping persistent agents on surfaces. The reflectivity of liquid-contaminated natural surfaces is generally not well described using simple reflectance models. Microscopic masking and shadowing effects are generally not accounted for in many models of surface reflectivity. It is typically necessary to treat natural surfaces as a collection of randomly-oriented facets in order to address masking and shadowing effects. Many natural surfaces are highly irregular, and incident light undergoes multiple reflections prior to exiting the medium. Effective models for granular media treat the material as a collection of particles with wavelength-dependent scattering albedos in order to address multiple scattering effects. Thin film models of LWIR surface reflectance generally presume a single reflection from a contiguous surface. For granular media, where particle sizes are comparable to the wavelength of incident light, and for solid surfaces which are rough on the length scale of the incident wavelength, diffraction effects are significant and need to be included in the reflectance model. For liquids which are well dispersed in the surface medium, the reflectivity of the contaminated medium may be better modeled in terms of a volumetrically-averaged refractive index rather than as discrete layers of materials with differing refractive indices. The appropriate model depends on the length scale of the internal surface roughness/particle size. PHASE I: Utilize modeling techniques to analyze IR reflectance and emittance data from thin layers of a non-volatile liquid simulant that have been applied to a variety of natural and man-made surfaces. Data analysis of the LWIR region will be emphasized. For this study, the stimulant will consist of a low-volatility silicon-based oil with strong absorption features in the 800 to 1200 wavenumber region. Surfaces should include dirt, sand, grass, concrete, and asphalt. Identify gaps in existing models and determine methods for improvement of existing models. PHASE II: Develop a physics-based model that can be used to predict the IR signature of a liquid stimulant on surfaces. The physics-based model should depend on the physical properties of the liquid contaminant, including complex index of refraction, vapor pressure, viscosity, etc. Design and build a computer program for prediction and automated data analysis of the spectral response of liquid contaminants on surfaces. Demonstrate ability to the new model to predict and analyze the IR signature of the simulant on a variety of surfaces. Using the physics-based model along with the known physical properties of persistent chemical agents, predict the spectral response of the liquid agents on a variety of surfaces and predict the utility of IR reflectance and emittance measurements as a method of detecting low-level contamination. PHASE III DUAL USE APPLICATIONS: Further research and development during Phase III efforts will be directed toward refining a final working model, incorporating modifications based on results from simulations and tests conducted during Phase II, and improving packaging and graphic interfaces to meet U.S. Army CONOPS and end-user requirements. The fundamental mathematical, computational, and statistical tools developed in this program will have broad impact across several avenues of defense applications. Examples include sensor, intelligence, biological, logistical, and other DOD-critical applications. Commercial applications of IR imaging include pollution monitoring and mineral exploration. 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