Tanya L. Myers

Polycyclic aromatic hydrocarbons (PAHs) are environmentally and astrophysically significant toxic organic compounds, several of which are carcinogenic. Infrared spectroscopy provides a nondestructive, standoff method of detecting and monitoring PAHs. Analysis of any infrared measurements, however, is complicated by the fact that measured extinction depends not only on molecular absorption features but also on electromagnetic scattering due to particle size and morphology. Quantitative interpretation therefore requires knowledge of the morphologically independent optical properties of the material, specifically the complex refractive indices n(ν̃) and k(ν̃). This work reports n(ν̃) and k(ν̃) of seven condensed-phase PAHs: naphthalene, phenanthrene, pyrene, benzo[a]pyrene, benzo[e]pyrene, benzo[ghi]perylene, and coronene. Wavelength-dependent complex refractive indices were derived in the mid-infrared (7800–400 cm–1) at room temperature (296 K) using quantitative, gravimetric potassium bromide (KBr) pellet transmission spectroscopy. The precision of the method was evaluated through a variability study of 15 KBr pellets of pyrene. The resulting complex refractive indices are then applied to model pyrene aerosol absorption spectra, demonstrating their utility for future morphology-dependent identification and quantitative interpretation of PAHs in infrared observations.

Reflectance spectroscopy is notoriously confounding in that the spectral response is highly dependent upon morphology. Fortunately, all such perturbations are neatly encoded by the complex refractive index. Herein, we quantitatively model the infrared reflectance spectrum of a specularly flat pressed pellet sample of the birefringent compound, sodium nitrate. Single crystals of sodium nitrate were synthesized and spectroscopically analyzed using polarization-dependent single-angle reflectance spectroscopy. Once measured and validated, the optical constants were applied to model the pressed pellet reflectance spectrum. It was evident that an average of the anisotropic refractive indices was insufficient to account for the measured pellet reflectance. The Python package pyElli was used to calculate a basis set of orientation-dependent reflection spectra spanning the distinct φ and θ Euler rotations of the uniaxial crystal. When the population of orientations was allowed to vary freely in a spectral fit analysis, the fit-deduced orientations were tightly clustered along φ = 45°, hinting at residual anisotropy in the pressed pellet sample. Conversely, an equally valid spectral fit (with marginally worse fit metric) was obtained when the population was constrained to an isotropic distribution of orientations. Subsequent non-zero cross-polarization reflectance measurements likewise suggested anisotropy in the pellet. However, both grazing-incidence wide-angle x-ray scattering and scanning electron microscopy measurements revealed that the microcrystal orientations at the surface of the pressed pellet sample were isotropically distributed. Application of the measured complex refractive indices for modeling the reflectance spectrum of the pressed pellet, and rectification of these seemingly contradictory observations will be discussed.

Hyperspectral imaging (HSI) data cubes are typically evaluated via methods whereby the processed spectral data are matched via some algorithm to libraries of hundreds/thousands of reflectance or emittance spectra of solids, in which the reference data often consist of pure chemicals of just one morphology and are often recorded for a specific application. We introduce an approach that can conveniently account for many different sample forms. Specifically, we demonstrate that different sample morphologies can be detected with high efficacy using “synthetic” spectra generated from physical constants and fundamental laws of optics. Experimental HSI data of thin-layer analyte chemical samples were evaluated using physics-based synthetic infrared spectra calculated from the optical n and k vectors in combination with absorption and reflection models. Besides the outdoor HSI data, hemispherical reflectance spectra were also collected in the laboratory using the same sample planchets. When evaluating the HSI standoff data using the synthetic spectra from two such models, the analysis found chemical identification to be nearly perfect. Both optical models could not only detect the target analyte chemicals but were also able to estimate a layer thickness that was found to be in quite good agreement (few micrometer level or better).

Optical identification of liquid droplets, aerosols, or thin films is critical for many applications. While reference spectra are sometimes available for such measurements, they are not always applicable to the observed spectrum or the given sample morphology. Reference spectra for many forms can be modeled, however, if the n / k vectors (real and imaginary refractive indices) are available. In previous work we have reported protocols to determine the n / k vectors for dozens of liquids, primarily in the mid-infrared (MIR) spectral range from 7500 to 400 cm ^–1 . In this work we extend the spectral range into the near-infrared (NIR) region, demonstrating a method to measure and merge the data sets to create composite n / k data ranging from 10 000 to 400 cm ^–1 (1.0 to 25 µm) with absorbance fidelity spanning over four orders of magnitude, and vastly improved signal-to-noise in the NIR. The precision of the composite data is evaluated for three different liquids, focusing primarily on the steps for converting the raw absorbance spectra to k values. The variability in both MIR and NIR data as well as in the final n/k vectors is also investigated for several liquids. For typical liquids, the overall variability (reported as 2σ) in the final n and k -vectors is determined to be ∼0.4% and 3%, respectively. Finally, the derived n / k data are used to calculate absorbance spectra for aerosol droplets, showing marginal variability due to the typical measurement errors in the final n / k vectors.

Infrared spectroscopy is a well-established method for identifying solid, liquid, and gas-phase chemicals. Accurate infrared spectroscopic analysis requires reference libraries where library endmembers reflect all optical phenomena contributing to the observed spectra. Traditional spectral libraries most often contain molecular-based absorption spectra, but these do not account for the complex scattering effects that become significant when measuring aerosols. In this work, we combine the laboratory-derived, wavelength-dependent complex optical vectors (n/k) of liquid dioctyl sebacate (DOS) with Mie scattering theory and the Beer–Lambert law to generate synthetic infrared transmission spectra of aerosolized DOS. Additionally, we record experimental infrared transmission spectra using an FTIR spectrometer coupled to a simple aerosol chamber filled with a quantified number size distribution of aerosolized DOS. The modeled and measured spectra show strong agreement, with Mie scattering effects clearly altering the overall spectral shape as well as the positions and profiles of absorption features. The results demonstrate that synthetic spectra generated from n/k values can reliably capture aerosol-specific spectral behavior and thus serve as a foundation for building scalable, physics-based aerosol reference libraries to enable infrared spectroscopic detection of aerosols.

Background: Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims: Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods: Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO2, CH4, H2, C6H6O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results: Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion: Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications: These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

The effects of light scattering and refraction play significantly different roles for aerosols than for bulk materials, making it challenging to identify aerosolized chemicals using traditional spectral methods or spectral reference libraries. Due to a potentially infinite number of particle morphologies, sizes, and compositions, constructing a database of laboratory-measured aerosol spectra is not a practical solution. Here, as an alternative approach, the measured n/k optical vectors of two example organic materials (diethyl phthalate and D-mannitol) are used in combination with particle absorption / scattering theory (Mie theory and FDTD) and the Beer-Lambert law to generate a series of synthetic infrared transmission / scattered light spectra. The synthetic spectra show significant differences versus simple slab transmission spectra, even for small changes in particle size (e.g., 5 vs. 10 µm) for both single particles and ensembles, potentially serving as useful reference data for aerosol sensing. For spherical single particles with diameters of 1 to 10 µm, FDTD simulations predict changes in the magnitudes of spectral shifts and the shapes of the peaks vs. particle size with only small deviations from Mie theory predictions, yet reliably capture the direction of the shifts. Typical spectral peak shifts in the longwave infrared correspond to Δλ ∼0.20 µm (∼34 cm^-1) when compared to corresponding slab transmission spectra. Additionally, synthetic spectra generated from the n/k values derived using two different methods (KBr pellet transmission and single-angle reflectance) are compared using the Mie theory model.

Identification of solids via infrared reflection spectroscopy requires a spectral library of all solids likely to be encountered. A confounding factor in populating such a spectral library is that the reflectance spectra of solids vary with their form, including particle size, film thickness, and substrate. To reduce the efforts of experimentally constructing such a library, an alternate strategy is to use the wavelength-dependent optical constants, n and k, of a solid to calculate a series of reflectance spectra corresponding to each scenario or morphology. Because most n/k measurements are best performed on mm-sized crystals, however, the challenge of determining the optical constants increases when a solid is only readily available as a powder, as is often the case. Some organic solids, such as caffeine, are both unavailable in large crystals and difficult to press into pellets. In this study, the infrared optical constants, or complex refractive indices, of caffeine were determined using three different methods: single-angle reflectance, infrared spectroscopic ellipsometry, and quantitative absorbance measurements of KBr pellets. The n and k values derived through each method were used to model the hyperspectral imaging reflectance spectrum of a caffeine film on a steel planchet. Over 1,110 – 870 cm^-1, the single-angle reflectance-derived n and k had the best correlation with the experimental spectrum. These results suggest different organic solids may require different methods to determine the most accurate infrared complex refractive indices for synthetic spectral libraries.

Spectroscopic identification of aerosolized chemical threats is challenging due to the complex nature of the photon/particle interaction, as well as the diversity of possible particle sizes, morphologies, and compositions. Constructing a database of laboratory - measured transmittance spectra that covers each permutation is not practical. However, calculation of the spectra using the measured optical constants as a function of wavenumber, n(ν) and k(ν), for each of the liquids and/or solids composing the aerosol particles provides a viable alternative. These synthetic spectra (vis-à-vis laboratory-measured spectra) can be used to identify chemicals of interest and subtract out background interferents in field measurements. Using a well-established multiple pathlength approach, we measure the optical constants n/k for liquids that may be found as chemical species of interest or common background interferents aerosolized in plumes. These measurements are also used to generate several test case aerosol synthetic spectra.