16 May 2018 Modeling liquid organic thin films on substrates
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Abstract
We present the rationale, methods, and results of modeling of thin film organic liquids on various substrates. These liquids may coat surfaces (substrates) either as a result of their production, dispersal via aerosols or spills. Identification of unknown coated surfaces using either reflectance or emittance spectroscopy cannot be accomplished simply through reference to reflectance signature libraries since neither the thickness of the liquid layer nor the substrate type is known beforehand and both contribute to the signature. Liquid spectral libraries offer the complex index of refraction (n,k) as a function of wavelength which by itself is useful only for thick (bulk) liquid layers via computation of reflectance and transmittance coefficients using the Fresnel equations. Thin liquid layers both reflect and refract incident light in combination with reflectance from the substrate. We show modeling of various organic liquids on substrates using commercial thin film design and modeling software, as well as Monte Carlo ray tracing software to demonstrate the variety of potential signatures encountered that depend on the thickness of the liquid layer as well as the characteristics of the substrate (metal or dielectric). These substrates give rise to transflectance behavior, while many dielectric substrates have rich absorption features that provide complex signatures that combine attributes of both the liquid and the substrate. Knowledge of the complex index of refraction of both target liquids and substrates is essential in order to synthesize spectra necessary in the application of target identification algorithms.
Conference Presentation
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Bruce E. Bernacki, Bruce E. Bernacki, Timothy J. Johnson, Timothy J. Johnson, Tanya L. Myers, Tanya L. Myers, Thomas A. Blake, Thomas A. Blake, } "Modeling liquid organic thin films on substrates ", Proc. SPIE 10629, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XIX, 1062916 (16 May 2018); doi: 10.1117/12.2299873; https://doi.org/10.1117/12.2299873
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