Timothy J. Johnson

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.

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.

During fiscal years 2024 and 2025, the U.S. Department of Energy’s Hanford Field Office commissioned Pacific Northwest National Laboratory to conduct applied research aimed at reducing the cost, time, and uncertainty associated with in situ treatment of vadose zone contaminants at the Hanford Site. This report outlines the integration of three key research efforts into a meso-scale demonstration designed to advance field-scale solutions that aim to (1) optimize the delivery of chemical amendments to contaminated soils, (2) reduce uncertainty in amendment delivery performance assessment using advanced monitoring techniques, and (3) provide real-time insights into when and where amendment-induced precipitation reactions occur in the subsurface. To achieve these objectives, the tank-scale (~ 1 cubic meter) Geophysical Imaging of Flow and Transport (GIFT) system was developed. GIFT enables experimental testing of amendment delivery while incorporating automated multi-modal monitoring approaches, including pressure measurements, direct fluid sampling, and remote time-lapse geophysical imaging. The data generated from these monitoring techniques will serve as inputs for a generative artificial-intelligence-driven digital twin – a numerical simulation model designed to honor observed data while quantifying uncertainty in simulation accuracy. Using this simulator, researchers will refine an amendment injection strategy to maximize delivery efficiency within a low-permeability soil zone. Monitoring data will be interpreted through simulated outputs to enhance understanding of the injection process. The efficacy of this integrated approach will be evaluated through direct sampling at the conclusion of the experiment.

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.

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for efficient standoff processing: atmospheric compensation and target detection. The atmospheric compensation method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. The compensation can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information; no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced, which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested using longwave infrared data (7.7 to 11.7 μm) on 40 images of 24 solid and powder mineral targets placed at a 14-m standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” also significantly reduced the number of false detections.