PFAS remediation: Evaluating the infrared spectra of complex gaseous mixtures to determine the efficacy of thermal decomposition of PFAS.

Due to their widespread production and known environmental contamination, the need for the detection and remediation of per- and polyfluoroalkyl substances (PFAS) has grown quickly. While destructive thermal treatment of PFAS at low temperatures (e.g., 200-500 °C) is of interest due to lower energy and infrastructure requirements, the range of possible degradation products remains underexplored. To better understand the low temperature decomposition of PFAS species, we have coupled gas-phase infrared spectroscopy with a multivariate curve resolution (MCR) analysis and a database of high-resolution PFAS infrared reference spectra to characterize and quantify a complex mixture resulting from potassium perfluorooctanesulfonate (PFOS-K) decomposition. Beginning at 375 °C, nine prevalent decomposition products (namely smaller perfluorocarbon species) are identified and quantified.

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry.

Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well-established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal-organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high-throughput, non-destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra.

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error. After correcting the wavelength and intensity of the field data, SpecQuant displays the calculated mixing ratio versus the experimental data for each analyte along with the residual spectrum with any or all analyte fits subtracted for visual inspection of the fit and residuals. The software performance for multianalyte quantification was demonstrated using moderate resolution (0.5 cm-1) infrared spectra that were collected during the time-resolved infrared photolysis of methyl iodide.

PARASIAS: A new method for analyzing higher-order tensors with shifting profiles.

Higher-order tensor data analysis has been extensively employed to understand complicated data, such as multi-way GC-MS data in untargeted/targeted analysis. However, the analysis can be complicated when one of the modes shifts e.g., the elution profiles of specific compounds often with respect to retention time; something which violates the assumptions of more traditional models. In this paper, we introduce a new analysis method named PARASIAS for analyzing shifted higher-order tensor data by combining spectral transformation and the simple PARAFAC modeling. The proposed method is validated by applications on both simulated and real multi-way datasets. Compared to the state-of-art PARAFAC2 model, the results indicate that fitting of PARASIAS is 13 times faster on simulated datasets and more than eight times faster on average on the real datasets studied. PARASIAS has significant advantages in terms of model simplicity, convergence speed, the robustness to shift changes in the data, the ability to impose non-negativity constraint on the shift mode and the possibility of easily extending to data with multiple shift modes. However, the resolved profiles of PARASIAS model are always a little worse when the number of components in the data are larger than three and without using additional factors in PARASIAS model. In such cases, more components are necessary for PARASIAS to model the data than that would be needed e.g., by PARAFAC2. The reason for this is also discussed in this work.

Identification of Uranium Minerals in Natural U-Bearing Rocks Using Infrared Reflectance Spectroscopy.

The identification of minerals, including uranium-bearing species, is often a labor-intensive process using X-ray diffraction (XRD), fluorescence, or other solid-phase or wet chemical techniques. While handheld XRD and fluorescence instruments can aid in field applications, handheld infrared (IR) reflectance spectrometers can now also be used in industrial or field environments, with rapid, nondestructive identification possible via analysis of the solid's reflectance spectrum providing information not found in other techniques. In this paper, we report the use of laboratory methods that measure the IR hemispherical reflectance of solids using an integrating sphere and have applied it to the identification of mineral mixtures (i.e., rocks), with widely varying percentages of uranium mineral content. We then apply classical least squares (CLS) and multivariate curve resolution (MCR) methods to better discriminate the minerals (along with two pure uranium chemicals U3O8 and UO2) against many common natural and anthropogenic background materials (e.g., silica sand, asphalt, calcite, K-feldspar) with good success. Ground truth as to mineral content was attained primarily by XRD. Identification is facile and specific, both for samples that are pure or are partially composed of uranium (e.g., boltwoodite, tyuyamunite, etc.) or non-uranium minerals. The characteristic IR bands generate unique (or class-specific) bands, typically arising from similar chemical moieties or functional groups in the minerals: uranyls, phosphates, silicates, etc. In some cases, the chemical groups that provide spectral discrimination in the longwave IR reflectance by generating upward-going (reststrahlen) bands can provide discrimination in the midwave and shortwave IR via downward-going absorption features, i.e., weaker overtone or combination bands arising from the same chemical moieties.

Passive standoff detection of RDX residues on metal surfaces via infrared hyperspectral imaging.

Hyperspectral images of galvanized steel plates, each containing a stain of cyclotrimethylenetrinitramine (RDX), were recorded using a commercial long-wave infrared imaging spectrometer. Demonstrations of passive RDX chemical detection at areal dosages between 16 and 90 microg/cm(2) were carried out over practical standoff ranges between 14 and 50 m. Anomaly and target detection algorithms were applied to the images to determine the effect of areal dosage and sensing distance on detection performance for target RDX. The anomaly detection algorithms included principal component analysis, maximum autocorrelation factors, and principal autocorrelation factors. Maximum difference factors and principal difference factors are novel multivariate edge detection techniques that were examined for their utility in detection of the RDX stains in the images. A target detection algorithm based on generalized least squares was applied to the images, as well, to see if the algorithm can identify the compound in the stains on the plates using laboratory reflection spectra of RDX, cyclotetramethylenetetranitramine (HMX), and 2,4,6-trinitrotoluene (TNT) as the target spectra. The algorithm could easily distinguish between the nitroaromatic (TNT) compound and the nitramine (RDX, HMX) compounds, and, though the distinction between RDX and HMX was less clear, the mean weighted residuals identified RDX as the stain on the plate. Improvements that can be made in this detection technique are discussed in detail. As expected, it was found that detection was best for short distances and higher areal dosages. However, the target was easily detected at all distances and areal dosages used in this study.

A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging.

In hyperspectral analysis, PLS-discriminant analysis (PLS-DA) is being increasingly used in conjunction with pure spectra where it is often referred to as PLS-Classification (PLS-Class). PLS-Class has been presented as a novel approach making it possible to obtain qualitative information about the distribution of the compounds in each pixel using little a priori knowledge about the image (only the pure spectrum of each compound is needed). In this short note it is shown that the PLS-Class model is the same as a straightforward classical least squares (CLS) model and it is highlighted that it is more appropriate to view this approach as CLS rather than PLS-DA. A real example illustrates the results of applying both PLS-Class and CLS.

Strategies for detecting organic liquids on soils using mid-infrared reflection spectroscopy.

Stand-off monitoring for chemical spills can provide timely information for cleanup efforts, and mid-infrared reflection spectroscopy is one approach being investigated for spill detection. Using laboratory data, anomaly and target detection strategies were examined for the detection of four different low-volatility organic liquids on two different soil types. Several preprocessing and signal-weighting strategies were studied. Anomaly detection for C-H bands was good using second-derivative preprocessing and provided similar performance to that of target detection approaches such as generalized least-squares and partial least-squares, with detections at soil loads of approximately 3-6 microg/cm2 a real dosage. Good performance was also found for the detection of P=O, O-H, and C=O stretching vibrational modes, but the optimal strategy varied. The simplicity and generality of anomaly detection is attractive; however, target detection provides more capability for classification.

Multivariate curve resolution applied to infrared reflection measurements of soil contaminated with an organophosphorus analyte.

Multivariate curve resolution (MCR) is a powerful technique for extracting chemical information from measured spectra of complex mixtures. A modified MCR technique that utilized both measured and second-derivative spectra to account for observed sample-to-sample variability attributable to changes in soil reflectivity was used to estimate the spectrum of dibutyl phosphate (DBP) adsorbed on two different soil types. This algorithm was applied directly to measurements of reflection spectra of soils coated with analyte without resorting to soil preparations such as grinding or dilution in potassium bromide. The results provided interpretable spectra that can be used to guide strategies for detection and classification of organic analytes adsorbed on soil. Comparisons to the neat DBP liquid spectrum showed that the recovered analyte spectra from both soils showed spectral features from methyl, methylene, hydroxyl, and P=O functional groups, but most conspicuous was the absence of the strong PO-(CH2)3CH3 stretch absorption at 1033 cm(-1). These results are consistent with those obtained previously using extended multiplicative scatter correction.

Error analysis for estimation of trace vapor concentration pathlength in stack plumes.

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data. These data were used with a classical least-squares-based estimator in an error analysis to obtain estimates of different sources of concentration-pathlength quantification error in the remote sensing problem. Contributions to the overall quantification error were the sum of individual error terms related to estimating the background, atmospheric corrections, plume temperature, and instrument signal-to-noise ratio. It was found that the quantification error depended strongly on errors in the background estimate and second-most on instrument signal-to-noise ratio. Decreases in net analyte signal (e.g., due to low analyte absorbance or increasing the number of analytes in the plume) led to increases in the quantification error as expected. These observations have implications on instrument design and strategies for quantification. The outlined approach could be used to estimate detection limits or perform variable selection for given sensing problems.