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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Using synthetic infrared spectra derived from n/k optical constants for standoff detection of chemical deposits

We report results from a recent field experiment to test the validity of using physics-based synthetic infrared spectra to serve as endmembers in a spectral database targeted at chemical deposits. Specifically, the optical constants n and k, (the real and imaginary part of the refractive index) were used to first model infrared reflectance spectra for different thicknesses of chemical layers (e.g. acetaminophen, methylphosphonic acid – MPA, etc.) on various conducting and insulating substrates such as aluminum, wood, and glass. In the experimental portion of the research, thin films of the solid and liquid analytes were deposited onto such substrates to form micron-thick layers of the analytes at different thicknesses: Standoff data from an imaging instrument were then recorded and analyzed to not only identify the different analytes, but also quantify the layer/deposit thickness. To gauge success, the detection results using the synthetic data were compared to the results from hemispherical reflectance (HRF) spectra that were collected for the same sample planchets measured in the field via standoff methods. Preliminary results indicate good agreement between the synthetic reference data as compared to the lab-measured HRF data in terms of their ability to quantitatively reduce longwave infrared data. Specifically, modeled IR spectra for acetaminophen on an aluminum planchet at various thicknesses (1, 2, 5, 10, 15, and 20 µm) were synthesized and compared with standoff field reflectance data as well as HRF laboratory reflectance spectra for two samples: a 5.2 µm- and 12.8 µm-thick layer of acetaminophen on aluminum. Using a first-order approximation, analysis of the field data estimates the thicknesses of the samples to be 2 and 10 µm for the two samples, respectively, while the HRF laboratory data yields thickness estimates of between 5-10 µm and 10 µm, respectively. Both yield reasonable estimates, with the uncertainty most likely due to factors yet to be accounted for in the synthetic spectra such as light scattering.

complex index of refraction, optical constants, hy↗
DOE OSTI2024

A method for generating quantitative vapor-phase infrared spectra of solids: results for phenol, camphor, menthol, syringol, dicyclopentadiene and naphthalene

Here, a method is presented to generate quantitative vapor-phase infrared spectra from substances that naturally occur as solids with moderate volatility. The solid is gravimetrically dissolved into a solvent that has few infrared spectral features, typically CS 2 and CCl 4 separately. The solution is flowed at a constant rate from a linearly pumped syringe into a metered stream of nitrogen carrier gas regulated by a mass flow controller. The analyte/solvent mix is flash vaporized by volatilizing the solution across a heated stainless-steel surface as it emanates from the syringe tip. The N 2 gas-solution mixture is flowed into a long-path White cell thermostatted at a desired temperature, the long optical path compensating for the modest analyte mixing ratio. A composite spectrum is generated from typically ten or more 760-Torr pressure-broadened spectra over the 600 to 6500 cm -1 spectral range at 0.1 cm -1 spectral resolution. The solid analytes reported here using this novel technique include dicyclopentadiene, menthol, syringol, phenol, camphor, and naphthalene.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Temperature-dependent diffuse reflectance measurements of ceramic powders in the near- and mid-infrared spectra

Radiative properties are critical to quantify radiative energy fluxes between surfaces and in participating media. However, there is limited experimental data on temperature-dependent radiative properties of materials. Herein, this work focuses on experimentally measuring temperature-dependent diffuse reflectance in the near- and mid-infrared spectra (1–20 μm) for ceramic particles with applications as heat-transfer and thermal-storage media in concentrated solar power (CSP) plants. Specifically, a commercially available sintered bauxite proppant, ACCUCAST ID80, and its primary chemical constituents—alumina (Al 2 O 3 ) and silica (SiO 2 )—are measured in powder form using a Fourier transform infrared spectrometer (FTIR) coupled with a specialized diffuse reflectance accessory and a heated stage. Room-temperature diffuse reflectance measurements show increased absorption in tests with greater mass fractions of the ceramic samples. There is a strong correlation in the measured reflectance spectra of ACCUCAST with alumina and silica in the spectral range 2000–500 cm –1 (5–20 μm). Whereas, for shorter wavelengths (< 5 μm), the absorptance for ACCUCAST is greater than the absorptance for alumina and silica, indicating contributions from other chemical species present in the composite material. For the first time, temperature-dependent diffuse reflectance measurements are reported for ACCUCAST up to 1000 °C. These results are compared against those of alumina and silica through the calculation of a thermal emittance. All three materials exhibit a calculated emittance of ~0.9 at room temperature. However, this value decreases to ~0.6 for ACCUCAST and drops to less than 0.4 for alumina and silica at 1000 °C. Thermal cycling in air at 1000 °C resulted in a visible color change from dark grey to light orange for ACCUCAST and a subsequent larger increase in reflectance for wavelengths less than 5 μm as compared to ACCUCAST thermally cycled at 1000 °C in vacuum. Alumina and silica spectra proved to be largely unaffected by thermal cycling under atmospheric conditions. Overall, this study establishes a powerful technique for the characterization of radiative properties of particulate materials as a function of temperature and presents a detailed case study of ACCUCAST, a candidate for next-generation particle-based CSP.

14 SOLAR ENERGY↗
DOE OSTI2022

Raman and infrared spectra of plutonium (IV) oxalate and its thermal degradation products

For over 80 years, plutonium dioxide has been routinely produced via thermal decomposition of hydrated plutonium(IV) oxalate. Despite the longstanding utility of this process, the chemical structures of starting materials and intermediates produced during this thermal conversion remain ill-defined. To help resolve this uncertainty, we measured high-resolution Raman and infrared spectra of Pu(C 2 O 4 ) 2 ·6H 2 O that was heated to 25, 100, 220, 250, 350, and 450 °C in air. Our measurements show that Pu(C 2 O 4 ) 2 ·6H 2 O has a rich vibrational spectrum with at least 15 Raman bands between 180 cm -1 and 1900 cm -1 and 9 infrared bands between 800 cm -1 and 4000 cm -1 . As Pu(C 2 O 4 ) 2 ·6H 2 O is heated, water is liberated, and the oxalate ligand decomposes to produce plutonium oxycarbide species. When heated to 350 °C or higher, vibrational spectra are consistent with PuO 2 with some residual carbon-containing species. Full vibrational spectra, powder X-ray diffraction, and scanning electron microscopy measurements of Pu(C 2 O 4 ) 2 ·6H 2 O and its thermal degradation products are presented herein along with approximate assignments for observed spectral bands. These data can be used to validate and potentially improve existing computational models that describe the chemical structure of compounds produced during thermal degradation of plutonium (IV) oxalate. Given the utility of plutonium (IV) oxalate in synthesizing plutonium dioxide, these results are expected to provide value in the fields of nuclear fuel processing, nuclear nonproliferation, and nuclear forensics.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗
DOE OSTI2025

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.

Neural Network↗
DOE OSTI2025

Using Multiple Isotope-Labeled Infrared Spectra for the Structural Characterization of an Intrinsically Disordered Peptide

Intrinsically disordered proteins (IDPs) rapidly interconvert between conformers, requiring an ensemble description. This complicates their experimental characterization, and force field limitations pose challenges for their simulation. Here, in this work, we use isotope-labeled and unlabeled infrared (IR) spectra to reweight simulated ensembles of the elastin-like peptide GVGVPGVG, a paradigmatic disordered peptide. By comparing the results obtained with different spectra, we explicitly show that the weights are underdetermined by the ensemble averaged data. We identify which labels and frequency regions maximize structural information while minimizing sensitivity to simulation error and show that these regions report on whether the peptide makes specific interactions. Our work shows the importance of incorporating simulations and simulated spectra at the planning stages of isotope-labeled IR experiments and more generally provides a framework for interpreting IR data for IDPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

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 to 500oC) 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 detect and quantify a complex mixture resulting from potassium perfluorooctanesulfonate (PFOS-K) decomposition. Nine prevalent decomposition products (namely smaller perfluorocarbon species) are identified and quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Measuring accurate optical constants of uranium minerals for use in optical modeling of infrared spectra

Knowledge of the bulk optical constants n and k of solids or liquids allows researchers to accurately predict the absorption, reflection, and scattering properties of materials for different physical forms. Indeed, chemically complex materials such as minerals can have an almost limitless variety of morphologies, particle sizes, shapes, and compositions, but the optical properties of such species can be predicted if the optical constants are known. For species such as minerals, however, there can be additional challenges due to e.g. hydration or dehydration during the course of the optical constants measurement. Here, we describe the protocols to obtain the bulk optical constants n and k of uranium-bearing minerals and ores such as uraninite or autunite. If quality n and k data are at hand, the (infrared) reflectance spectra can be predicted for different particle sizes and morphologies and the modeling results for various scenarios can be derived.

infrared (IR) spectroscopy, optical constants, ura↗
DOE OSTI2023

What do far-infrared spectra of solitary water in “water-in-solvent” systems reveal about water’s solvation and dynamics?

Classical molecular dynamics simulations of water in ionic and dipolar solvents were used to interpret the far-infrared (FIR) rotation/libration spectra of “solitary water” in terms of water’s rotational dynamics and interactions with solvents. Seven solvents represented by nonpolarizable all-atom force fields and a series of idealized variable-charge solvents were used to span the range of solvent polarities (hydrogen bonding) studied experimentally. Simulated spectra capture the solvent dependence observed, as well as the relationship between the frequencies of water libration (νL) and OH stretching bands (νOH). In more strongly interacting solvents, simulated νL are ∼20% higher than those of experiment. In all solvents, the simulated spectra are composites of rotational motions about the two axes perpendicular to water’s dipole moment, and the different frequencies of these two motions are responsible for the breadth of the libration band and the bimodal shape observed in halide ionic liquids. Simulations overestimate the separation of these two components in most solvents. The character of water rotational motions changes markedly with solvent polarity, from quasi-free rotation in nonpolar and weakly polar solvents to highly constrained libration in strongly hydrogen bonding environments. The changeover to librational motions dominating the spectrum occurs between solvents such as benzene (νL ∼ 250 cm−1) and acetonitrile (νL ∼ 400 cm−1). For solvents in the latter category, the mean frequency of the experimental FIR band provides a direct measure of mean-squared torques and, therefore, force constants associated with interactions constraining water’s librational motion.

Chemistry↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. I. Conformer- and isomer-resolved infrared spectra

Transient carbon-centered hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation are characterized by infrared (IR) action spectroscopy under jet-cooled conditions with selective detection of hydroxyl (OH) radical products. Two distinct •QOOH isomers with radical sites at a primary carbon of one of the methyl groups (β-Me) or a secondary carbon (β-Et) of the ethyl group are identified by comparison with theoretically calculated IR absorption features for multiple conformers, including C–O, ethyl, and O–O torsions of the two isomers. Here, a master-equation analysis is developed and utilized to explore the conformational cooling process and the resultant conformer population distributions of the two isomers. Most of the IR features observed, including those in the strong fundamental and first overtone OH stretch regions as well as weaker combination bands involving OH stretch with torsion or OOH bend, are in good accord with computed anharmonic frequencies for the most populated conformers of the •QOOH_Et isomer. Relatively weak IR features most evident in the fundamental and first overtone OH stretch regions are ascribed to multiple conformers of the less stable •QOOH_Me isomer, along with a weak feature that is uniquely attributed to a combination band involving asymmetric CH 2 stretch and HCH bend of •QOOH_Me.

Chemical reaction jet spectroscopy↗
DOE OSTI2026

Correlation between infrared spectra features and coverage of different adsorption sites for the NO/Pd(111) system

This study investigates the adsorption of nitric oxide (NO) on Pd(111) surfaces at 200 K using infrared reflection absorption spectroscopy (IRRAS). Peak positions and areas are used as proxies for tracking coverage of different species, serving as groundwork for in-situ experiments that need real-time tracking of surface species. Here, we derived a mathematical model correlating NO coverage with dose, consistent with the Molecular Langmuir Model. Distinct correlations are observed between coverage and spectral features (peak position and peak area for the NO vibration): at lower coverages (θ<0.6), the total integrated peak areas are linearly corelated to coverage; at higher coverages (θ>0.6), the peak center for a compressed-hollow site becomes the primary coverage indicator, showing a linear relationship between wavenumber and coverage. Note that this study addresses coverages below saturation. These findings refine our understanding of NO adsorption and establish a foundation for real-time in situ transient kinetics studies of chemical reactions involving NO on Pd(111).

25 ENERGY STORAGE↗
DOE OSTI2023

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSCKSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e. g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures.

58 GEOSCIENCES↗
DOE OSTI2022

Near-infrared spectra and molar absorption coefficients of trivalent lanthanides dissolved in molten LiCl–KCl eutectic

Determining the concentration of the dissolved lanthanide species in LiCl–KCl eutectic salt is important to the development of pyrochemical reprocessing of used nuclear fuel. In this process, lanthanide fission products are found dissolved in the electrorefiner electrolyte in their trivalent oxidation state. The presence of dissolved trivalent lanthanides increases the liquidus temperature of the electrolyte mixture and can lead to the formation of insoluble oxide or oxychloride phases and must therefore be continuously monitored and controlled during the operation. Absorbance spectroscopy is a promising method for continuous measurement of the concentration of lanthanides and other elements dissolved in the electrolyte. The absorption of light by elements is linearly proportional to the concentration of the element for relatively dilute solutions according to the Beer-Lambert law. Although measurement of the absorption of ultraviolet and visible range light by lanthanides in LiCl–KCl eutectic molten salt have been explored previously, near infrared (NIR) absorption spectroscopy has received far less attention. It may, however, provide a better analytical signal when insoluble phases are present due to less Raleigh scattering compared to shorter wavelength radiation. Additionally, it may allow for concentration determination for certain elements using NIR absorption features where UV and visible range features are overlapping with features from other species. In this study, we report the UV–Vis–NIR spectra of the trivalent lanthanide chlorides of neodymium, samarium, and dysprosium in LiCl–KCl eutectic. Molar absorption coefficients are reported for analytically useful absorption maxima, with a focus on the molar absorption coefficients for NIR absorption maxima which have not been reported previously. Additionally, we observe a NIR-range absorption band of Nd3+ which was previously predicted but never experimentally observed. Here, we compare the calculated crystal field levels to the newly observed absorbance band and find them to be in good agreement with previous predictions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Experimental and calculated infrared spectra of disubstituted naphthoquinones

In recent years there has been interest in incorporating substituted 1,4-naphthoquinones (NQs) into the A 1 binding site in photosystem I (PSI) photosynthetic protein complexes. This interest in part stems from the considerably altered bioenergetics of electron transfer that occur in PSI with such substitutions. Time resolved FTIR studies of PSI complexes with disubstituted NQs incorporated have and currently are being undertaken, and with this in mind it is worth considering FTIR absorption spectra of these disubstituted NQs in solution. Here we present FTIR absorbance spectra for 2-bromo-3-methyl-1,4-naphthoquinone (BrMeNQ), 2-chloromethyl-3-methyl-1,4-naphthoquinone (CMMeNQ) and 2-ethylthio-3-methyl-1,4-naphthoquinone (ETMeNQ) in tetrahydrofuran (THF). The FTIR spectra of these di-substituted naphthoquinones (NQs) were compared to FTIR spectra of 2-methyl-3-phytyl-1,4-naphthoquinone [phylloquinone (PhQ)], 2,3-dimethyl-1,4-naphthoquinone (DMNQ), and 2-methyl-1,4-naphthoquinone (2MNQ). To aid in the assignment of bands in the experimental spectra, density functional theory (DFT) based vibrational frequency calculations for all the substituted NQs in solution were undertaken. The calculated and experimental spectra agree well. By calculating normal mode potential energy distributions, unambiguous quantitative band assignments were made. The calculated and experimental spectra together make predictions about what may be observable in time resolved FTIR difference spectra obtained using PSI with the different NQs incorporated. As a result, time resolved FTIR difference spectra are presented that support these predictions.

59 BASIC BIOLOGICAL SCIENCES↗