This study examines an application of simulated single-particle spectra for analysis of IR scattering by suspensions of small particles. Particularly, the case of aerosols comprised of solid particles is explored. These particles vary in size, shape, and orientation with respect to the incident light. Single particle scattering is numerically calculated and the simulated data is surveyed. The scattering of particle ensembles is considered and a prototype system consisting of a sparse distribution of particles is evaluated.

The size of particles typically present in aerosol clouds are in the range of 0.1-10 mu m, which is within one order of magnitude of the infrared (IR) wavelengths in the molecular "fingerprint" region (approx. 6-12 mu m). This length scale is also close to the optical absorption depth for materials of interest. Consequently, IR scattering signatures of aerosols differ from those associated with reflectance from surfaces of bulk materials. Furthermore, the shape of particles is also a factor that affects IR scattering spectra. Accordingly, both aerosol particle size and morphology must be considered in the development of accurate models and reliable detection algorithms. This report describes recent advances concerning modeling of IR scattering signatures of micron-sized spherical particles (found in liquid aerosols) and irregularly-shaped particles ( found in solid aerosols). In our model, spherical particles are modeled using Mie scattering theory while non-spherical ones require numerical modeling, using finite-difference time-domain (FDTD) solvers. The model inputs involve particle optical constants (complex index of refraction - n and k), aerosol concentration, particle size (diameter) distribution and various shape parameters (for solid aerosols only). Our model addresses two detection scenarios: one where the signatures consist of back-scattered light only and the other where the portion of the forward scattered light which (diffusely) reflects off background surfaces is also collected. We discuss the effect of particle size and shape distribution on the IR signatures of aerosol clouds. We report on efforts to optimize our model such that a large number of spectra can be generated quickly and efficiently, which is a requirement for use in detection algorithms, both for training and usage. We also present preliminary results on machine learning approaches to develop detection algorithms capable of detecting aerosol clouds that have variable IR signatures due to different particle/size distributions. In this paper, we focus on liquid aerosols, while solid aerosols are discussed in a related conference paper.

We report a novel algorithm for generating optimized look-up tables suitable for rapid evaluation of various light scattering parameters and other hard-to-evaluate functions. Our method uses a stochastic algorithm to minimize the number of look-up table points needed while achieving high accuracy and speed. As an example, we present a general Mie scattering look-up table applicable to a large range of size parameters (0.02<x<200) and most materials (organics, inorganics, minerals, metals etc.) with real and imaginary parts of the refractive index ranging from 0.2 to 5 and 0 to 5, respectively. The look-up table is up to 3,500 times faster than evaluating the Mie analytical expressions (in Matlab). This method opens up new possibilities in detection algorithm development (e.g. large synthetic datasets for machine learning), inverse problems and all other problems where a large number of Mie scattering coefficients needs to be rapidly evaluated. Furthermore, this method is applicable to other, related scattering problems. For example, we also present look-up tables for scattering efficiencies for spheres on various substrates.

A clear understanding of sublimation kinetics is critical for developing detection techniques. Sublimation affects the shape and size of small particles as well as the environment. The particle characteristics are essential for various types of measurements including optical response. Molecular dynamic simulations were used to better understand the kinetics of both the condensation of molecules onto and the sublimation of molecules from the surfaces of explosives materials such as 2,4-dinitrotoluene and 2,4,6-trinitrotoluene. These studies were undertaken to better understand the persistence of trace quantities of particles on surfaces to aid efforts to optical detect strategies of explosives as well as physical harvesting approaches such as collection with swabs. Potential-energy-function parameters for the molecular dynamic (MD) simulations are designed and values for the probability of recondensation onto the surface (i.e., sticking coefficient) and the velocity distribution of molecules escaping the particle surface calculated. These values were compared with other experimental and simulation efforts for the studied materials.

Extraction of experimental spectrum features from target molecules, for purpose of their detection, can be achieved by comparison to template spectra within a database. This study continues presentation of the concept of using density functional theory (DFT). DFT-calculated spectra are well posed for comparison to measured spectra, to the extent of their scalability to larger space–time scales. Specifically, the focus of this study is the scalability of DFT-calculated IR spectra with respect to meso- and macroscales, characteristic of dielectric response as measured using different IR spectroscopies. A case-study analysis concerning IR spectra scalability for caffeine is described. Caffeine is only used as an example of analysis that can be applied to PFAS molecules, which are our major interest.

The transmission coefficient for a 6-function barrier is a convenient model for many technologically important applications relying on photoemission, simulations of wave packets, or modeling the narrow barrier of a normal- superconducting point contact. We examine an extension of the model to treat instead a function sequence (a rectangular barrier that approaches the behavior of a function in the limit of vanishing width). It is shown how the eigenstates of the sequence converge on the function barrier eigenstates, but more importantly, how the even and odd parity states depart from the 6-function limiting case. The exact eigenstates enable the time evolution of exponentially attenuated tunneling to be exactly evaluated, in contrast to numerical methods. The application is the inclusion of tunneling time effects in simulations of time-varying electron emission.

The concept of enabling drone-swarm engagement simulations using particle-dynamics models and near-neighbors tracking algorithms, motivated by SDI battle management, is examined. The general approach of using particle-dynamics models and near-neighbors tracking algorithms for modeling drone-swarm engagements is similar to nonequilibrium molecular-dynamics modeling of mixing dissimilar particulate materials. With respect to particle-dynamics representation of swarm-engagements, fundamental quantities that can represent characteristics of drone interactions, are interparticle potential functions, which are a function of drone-drone separation, the types of drones interacting, and the nature of the interaction. These potential functions provide formal representation of both deterministic and non-deterministic dronedrone interaction scenarios. The complexity of drone-swarm engagements, similar to that of SDI scenarios, characterized by small time-periods of engagement, multiple types of blue-red force interactions, and the requirement of near-neighbor target tracking, suggest that such a tool be necessary. The utility of the tool in creating potential-theory based control algorithms for swarm-on-swarm engagements is demonstrated using particle-dynamics simulations.

This report describes inverse spectral analysis of diffuse-reflectance spectra measured using Infrared Backscatter Imaging spectroscopy (IBIS). In IBIS, a tunable infrared laser illuminates a target while an infrared camera detects the backscatter. Target analytes are identified by analyzing the pattern of absorption dips in the detected backscatter and comparing them to the known or simulated reflectance spectra of hazardous materials. The backscatter spectrum is comparable to diffuse reflectance measured using a Fourier transform infrared (FTIR) spectrometer. The analysis methodology applied here entails iterative adjustment of spectra using phenomenological backgrounds. and estimation of absorbance using the Kubelka-Munk (KM) theory of diffuse reflectance. Applying spectrum-feature enhancement, measured with a field spectrometer, can provide a better estimation of dielectric response, which is for comparison to reference dielectric functions, for identification of target materials.

The analytic nature of the transmission coefficient for a δ-function barrier makes it a useful tool to examine a variety of technologically important applications, such as photoemission from semiconductors with an alkali coating, the examination of tunneling times for wave packets incident on a barrier, and for parameterizing tunneling through the narrow barrier of a normal-superconducting point contact. The analytic model of a δ-function barrier inside a confining well is extended to the finite height and width rectangular barrier (a delta-function sequence). Methods to exactly evaluate the eigenstates are given and their dependencies are examined. The time evolution of a superposition of the lowest eigenstates is considered for barriers having comparable Gamow tunneling factors so as to quantify the impact of barrier height and shape on time evolution in a simple and exact system and, therefore, serve as a proxy for tunneling time. Last, density profiles and associated quantum potentials are examined for coupled wells to show changes induced by weaker and wider barriers.

This study examines estimation of dielectric functions, based on the ability of pseudo-broadened DFT-calculated IR spectra to have very high correlation with measured IR-spectra, on the macroscale. For the case of dielectric-function estimation, one seeks by means of pseudo broadening, a best or most reasonable approximation of macroscale absorbance spectra using DFT spectra. Specifically, this study examines scalability of DFT-calculated IR spectra with respect to meso and macro scales, characteristic of dielectric response as measured using different IR spectroscopies. A case study analysis concerning scalability of IR spectra for caffeine is described.

Electron sources exploiting field emission generally have sharp geometries in the form of cones and wires. Often, they operate under elevated temperatures. A sharply curved emitter affects the emission barrier past which the electrons must be emitted via thermal-field processes, as does a space charge in metal-insulator-metal and metal-oxide-semiconductor devices: all can be examined using the Gamow factor θ(E) on which the general thermal-field equation is based. A methodology to evaluate θ(E) based on shape factor methods is given that emphasizes analytical methods, speed, and accuracy of execution and is applied to curvature and space-charge modified barriers characterized by the addition of a quadratic barrier term. The implications for thermal, field, and thermal-field emission are assessed. In addition to the known temperature rise that attends current through a wire, tapering of the emitter apex is a source of additional temperature increases, which are assessed using a simple model that provides an upper temperature limit appropriate for tip-on-post or poor thermally conductive materials.

A numerical-analytical model and simulations are described concerning diffuse reflectance for surface-distributed material particles on substrates. The model combines an analytical formulation of Mie-scattering theory and a numerical procedure based on Kramers-Kronig analysis. The results of simulations using this model are compared with experimental measurements of diffuse reflectance for material particles distributed on a glass surface. The purpose of these comparisons is estimating the influence of background contributions to spectral features due to resonant scattering from finite-size particles. Evaluating the sensitivity of spectrum-feature extraction methodologies with respect to these background contributions is significant for practial detection of target materials.

Hydrogen-bonding plays an important role in interactions of molecular structures, and is associated with distinct features in vibrational spectra of molecular systems. Interpretation of these features is essential for monitoring and control of structural changes and kinetic processes in large ensembles of molecular structures. Computational experiments based on molecular dynamics provide interpretation of vibrational-spectrum features. This report extends previously reported simulations concerning nerve-agent-sorbent binding for examination of spectral features correlated with annealing.

Identification of target molecules, based on spectrum-feature extraction by comparison of spectra, can be accomplished using signal templates having patterns associated with known materials. This study examines the concept of using IR spectra calculated using density functional theory (DFT) as signal templates. In principle, DFT calculated IR spectra should provide reasonable templates for comparison with IR spectral measurements associated with different types of detector schemes and complex spectral-signature backgrounds. In practice, however, there exists artifacts due to computational errors and model assumptions in the case of DFT calculated spectra, and artifacts due to measurement errors and experimental-design assumptions in the case of spectral measurements. Accordingly, the use of DFT calculated spectra as signal templates must consider these artifacts. In this study, case-study analysis of IR absorption spectra for a water contaminant of interest is presented, which demonstrates aspects of using DFT calculated IR spectra to determine the presence of target molecules.

This study describes a methodogy for spectrum-feature extraction from diffuse reflectance for distributions of materials on substrates, which is based on diffuse-reflectance theory and phenomenological multiplicative-factor decomposition of reflectance functions. Specifically, this methodology entails feature-extraction using reflectance-spectrum normalization with respect to phenomenological backgrounds. A mathematical analysis of the feature-extraction methodology with respect to its formulation is presented. In addition, results of inverse analyses demonstrating application of the methodology are described.

An electron wave packet tunneling through a barrier has a transmission (or "group delay") time tau(g) that, for a rectangular barrier, is commonly held to become independent of the barrier width L as the width increases (the McColl-Hartman effect). In the present study, it is shown that first, the McColl-Hartman effect for a rectangular barrier is dependent upon L as the Gamow tunneling factor theta(k) vanishes, and tau(g) is only independent of L when theta (k) is large; and second, for a triangular barrier to model field emission, although tau(g) can be large for small field, it vanishes when the energy matches the barrier height.

PFAS molecules are chain-linked carbon/fluorine atoms, widely distributed in the environment, and dangerously toxic biologically. Here is constructed, using density functional theory (DFT), a prototype database of IR spectra for detection of PFAS molecules. Extraction of spectrum features for target molecules from measured spectra can be achieved by comparison to template spectra within a spectrum database, which are sufficient approximations of target spectra. The concept of extracting spectral features is distinct from that of inverting reflectance or transmission spectra for determination of dielectric response functions. This study continues presentation of the concept of using DFT to calculate template spectra for practical detection of target substances, by comparison with spectra within databases. Specifically, the focus here is upon PFAS molecules, which include toxic and carcinogenic environmental contaminants, and whose detection based on IR spectroscopy is thus of great importance.