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DOE OSTI2024

Quantifying spectral albedo effects on bifacial photovoltaic module measurements and system model predictions

We provide a comprehensive analysis of the effect of spectral albedo on photovoltaic (PV) module measurements and system model predictions. We demonstrate how to account for albedo in indoor bifacial device measurements by adjusting the applied irradiance using the scaled rear irradiance method, exemplified on fabricated silicon heterojunction (SHJ) modules. System model performance is studied using a detailed 3D finite-element model, DUET, for fixed-tilt and horizontal single-axis tracked (SAT) arrays between 15 and 75°N. Spectral effects cause variations in measured SHJ module short-circuit current up to 2% and efficiency variation up to 0.3% abs. We further demonstrate that rear-side spectral mismatch factors (SMMs) resulting from including or omitting spectral albedo in PV system modeling vary between ±13%, while total (front+rear) SMMs vary up to 3%, depending on the deployment configuration and latitude. SAT array SMMs are weakly correlated with latitude, while fixed-tilt array SMMs increase with latitude, driven by an increasing proportion of ground-reflected light on the front-side of modules. Ground-reflections can constitute between 2% and 32% of total incident module irradiance, with notably high (>10%) contributions for fixed-tilt arrays at high latitude. Effects of spectral albedo are most significant for: (1) fixed-tilt deployments at high latitudes, (2) wide bandgap technologies such as perovskite and cadmium telluride cells, (3) albedos which vary steeply over the technology's absorption range, and (4) high albedo ground covers. Overall, we demonstrate that omitting spectral albedo effects can result in PV measurement and system-level modeling uncertainties on the order of several percent in these cases.

14 SOLAR ENERGY↗

DOE OSTI2022

An Improved Method for Coupling Hydrodynamics with Astrophysical Reaction Networks

Reacting astrophysical flows can be challenging to model, because of the difficulty in accurately coupling hydrodynamics and reactions. This can be particularly acute during explosive burning or at high temperatures where nuclear statistical equilibrium is established. We develop a new approach, based on the ideas of spectral deferred corrections (SDC) coupling of explicit hydrodynamics and stiff reaction sources as an alternative to operator splitting, that is simpler than the more comprehensive SDC approach we demonstrated previously. We apply the new method to a double-detonation problem with a moderately sized astrophysical nuclear reaction network and explore the time step size and reaction network tolerances, to show that the simplified-SDC approach provides improved coupling with decreased computational expense compared to traditional Strang operator splitting. This is all done in the framework of the Castro hydrodynamics code, and all algorithm implementations are freely available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

UV–Vis–NIR Reflectance Spectroscopy and Chemometrics for Monitoring Pu Directly on an Ion Exchange Column

Here, we present a fiber-optic UV–vis–NIR reflectance spectroscopy method for direct, noninvasive monitoring of Pu(IV) in a glass ion exchange column during dynamic loading and elution in a glovebox. A movable probe enables spatially resolved spectral acquisition along the column axis, capturing distinct features associated with Pu(IV) nitrate complexes during loading and free ions during elution. Principal component analysis was applied to extract the dominant spectral variance and resolve relative concentration profiles without requiring precise knowledge of optical penetration depth or species identity. This in situ approach reveals spatial gradients and speciation dynamics in real time, which provides actionable insight into Pu(IV) ion migration, resin saturation, and breakthrough behavior under evolving flow conditions. The method offers a practical, fiber-compatible strategy to monitor glass column–based separations for Pu and other lanthanides or actinides and to characterize metal–resin interactions in flow-through systems.

actinide↗

DOE OSTI2024

A FFT-based mesoscale continuum dislocation mechanics with defect energy: Applications to composites and polycrystals

A crystal plasticity elastoviscoplastic FFT (fast Fourier transform) formulation with a mesoscale continuum field dislocation mechanics model is presented, which incorporates a defect energy density that depends on GND densities and an associated material length scale. This allows to thermodynamically derive internal length scale dependent intra-crystalline backstress and Peach–Koehler force acting on GND densities. The model considers GND density evolution through a filtered numerical spectral approach, which is coupled with stress equilibrium through the elastoviscoplastic FFT algorithm. The discrete Fourier transform (DFT) method together with finite difference (FD) schemes is applied to solve both the backstress tensor and the Fourier–Green operator. Numerical results are first reported for two-phase laminate composites with plastic single crystal channels and elastic precipitates for shear loadings. Channel size effects are simulated and analyzed on the overall and local hardening behaviors during monotonous loadings. In addition, the evolutions of GND densities and the role of their associated backstress on size effects are examined during reversible shear loading. In a second part, the role of the defect energy internal length scale on polycrystal’s hardening during tension–compression is discussed. The results are compared to those obtained using FFT-based continuum field dislocation mechanics without defect energy.

36 MATERIALS SCIENCE↗

DOE OSTI2021

AFM-IR and s-SNOM-IR measurements of chemically addressable monolayers on Au nanoparticles

The performance of catalysts depends on their nanoscale properties, and local variations in structure and composition can have a dramatic impact on the catalytic reactivity. Therefore, probing the localized reactivity of catalytic surfaces using high spatial resolution vibrational spectroscopy, such as infrared (IR) nanospectroscopy and tip-enhanced Raman spectroscopy, is essential for mapping their reactivity pattern. Two fundamentally different scanning probe IR nanospectroscopy techniques, namely, scattering-type scanning near-field optical microscopy (sSNOM) and atomic force microscopy-infrared spectroscopy (AFM-IR), provide the capabilities for mapping the reactivity pattern of catalytic surfaces with a spatial resolution of ~20 nm. Herein, we compare these two techniques with regard to their applicability for probing the vibrational signature of reactive molecules on catalytic nanoparticles. For this purpose, we use chemically addressable self-assembled molecules on Au nanoparticles as model systems. We identified significant spectral differences depending on the measurement technique, which originate from the fundamentally different working principles of the applied methods. While AFM-IR spectra provided information from all the molecules that were positioned underneath the tip, the s-SNOM spectra were more orientation-sensitive. Due to its field-enhancement factor, the s-SNOM spectra showed higher vibrational signals for dipoles that were perpendicularly oriented to the surface. The s-SNOM sensitivity to the molecular orientation influenced the amplitude, position, and signal-to-noise ratio of the collected spectra. Ensemble-based IR measurements verified that differences in the localized IR spectra stem from the enhanced sensitivity of s-SNOM measurements to the adsorption geometry of the probed molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Infrared spectroscopy of the syn -methyl-substituted Criegee intermediate: A combined experimental and theoretical study

An IR–vacuum ultraviolet (VUV) ion-dip spectroscopy method is utilized to examine the IR spectrum of acetaldehyde oxide (CH3CHOO) in the overtone CH stretch (2νCH) spectral region. IR activation creates a depletion of the ground state population that reduces the VUV photoionization signal on the parent mass channel. IR activation of the more stable and populated syn-CH3CHOO conformer results in rapid unimolecular decay to OH + vinoxy products and makes the most significant contribution to the observed spectrum. The resultant IR–VUV ion-dip spectrum of CH3CHOO is similar to that obtained previously for syn-CH3CHOO using IR action spectroscopy with UV laser-induced fluorescence detection of OH products. The prominent IR features at 5984 and 6081 cm−1 are also observed using UV + VUV photoionization of OH products. Complementary theoretical calculations utilizing a general implementation of second-order vibrational perturbation theory provide new insights on the vibrational transitions that give rise to the experimental spectrum in the overtone CH stretch region. The introduction of physically motivated small shifts of the harmonic frequencies yields remarkably improved agreement between experiment and theory in the overtone CH stretch region. The prominent features are assigned as highly mixed states with contributions from two quanta of CH stretch and/or a combination of CH stretch with an overtone in mode 4. The generality of this approach is demonstrated by applying it to three different levels of electronic structure theory/basis sets, all of which provide spectra that are virtually indistinguishable despite showing large deviations prior to introducing the shifts to the harmonic frequencies.

Chemistry↗

DOE OSTI2021

Numerically exact generalized Green's function cluster expansions for electron-phonon problems

We generalize the family of approximate momentum average methods to formulate a numerically exact, convergent hierarchy of equations whose solution provides an efficient algorithm to compute the Green's function of a particle dressed by bosons suitable in the entire parameter regime. We use this approach to extract ground-state properties and spectral functions. Our approximation-free framework, dubbed the generalized Green's function cluster expansion (GGCE), allows access to exact numerical results in the extreme adiabatic limit, where many standard methods struggle or completely fail. We showcase the performance of the method, specializing three important models of charge-boson coupling in solids and molecular complexes: the molecular Holstein model, which describes coupling between charge density and local distortions, the Peierls model, which describes modulation of charge hopping due to intersite distortions, and a more complex Holstein + Peierls system with couplings to two different phonon modes, paradigmatic of charge-lattice interactions in organic crystals. Furthermore, the GGCE serves as an efficient approach that can be systematically extended to different physical scenarios, thus providing a tool to model the frequency dependence of dressed particles in realistic settings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular “fingerprints”), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Impact of Glass Irradiation on Laser-induced Breakdown Spectroscopy Diagnostics in the Visible and NIR Range

Increased absorption of optical materials arising from the exposure to ionizing radiation must be accounted for to accurately analyze the laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on a specifc example that mimics the diagnostics placed within a gas-cooled fast reactor coolant stream. Analysis is performed on the LIBS data measured with 1% Xe gas in an ambient He environment and the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. No significant change in the number of shots required to reach a 3σ detection level was observed up to 10 Mrad gamma dose and exposure to a 1.7×10 17 n/cm 2 neutron fluence. In contrast, the spectral dependence of absorption results in either a systematic overestimation or underestimation of line intensity ratios, depending on the line of interest selected for analysis. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation also leads to statistically significant changes in the calculated temperatures for calculations using Xe II lines only, lowering them from 8000±610 K to 6800±810 K for the case of exposure to the 1.7×10 17 n/cm 2 flux, and broadening the temperature range required for a 95% confidence interval. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation-attenuation is relatively small, or through analysis using the iterative Saha-Boltzmann method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A double peak in the seasonality of California's photosynthesis as observed from space

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIR$_v$) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

RTN-099: Photometric Transformation Relations for the LSST Data Preview 1

This technical note provides photometric transformation relations between the Vera C. Rubin Observatory's LSSTCam and LSSTComCam systems and other photometric systems. These transformations are derived using both synthetic and empirical data and are intended to support calibration and comparison across survey systems. We present both polynomial equations and lookup-table-based methods, depending on the available data and desired accuracy. The transformations are generally valid for stars with typical spectral energy distributions (SEDs), and caution should be used when applying them to objects with strong emission lines or atypical colors.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

RTN-125: Photometric Transformation Relations for the LSST Data Preview 2

This technical note provides photometric transformation relations between the NSF-DOE Vera C. Rubin Observatory's Data Preview 2 (DP2) and other photometric systems. These transformations are derived using both synthetic and empirical data and are intended to support calibration and comparison across survey systems. We present both polynomial equations and lookup-table-based methods, depending on the available data and desired accuracy. The transformations are generally valid for stars with typical spectral energy distributions (SEDs), and caution should be used when applying them to objects with strong emission lines or atypical colors.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.

Drgona, Jan↗

DOE OSTI2023

An infrared spectral database for gas-phase quantitation of volatile per- and polyfluoroalkyl substances (PFAS)

We report the construction of a database of vetted infrared spectra specifically targeting volatile fluorocarbon gases that may be emitted during thermal treatment of per- and polyfluoroalkyl substances (PFAS) to assist understanding of treatment processes and improve quantification. To populate this database, protocols derived from the Pacific Northwest National Laboratory (PNNL) infrared spectral database are used, curtailing the species selection for this data set. Each spectrum in the database is a weighted average derived from 10 or more individual measurements at different partial pressures (static method) or flow rates (dissemination method) to yield good fidelity of both strong and weak infrared signatures, with each composite spectrum ranging from = 6500 cm-1 to = 600 cm-1 with an apodized resolution of 0.112 cm-1. This resolution was chosen to fully resolve all spectral features, recognizing that atmospheric pressure broadening results in nearly all ro-vibrational lines having linewidths = 0.1 cm-1. As an example case, application of the database is demonstrated via identification and quantification of dominant 1H-perfluoroheptane and perfluorohept-1-ene fluorocarbon products resulting from thermal decomposition of perfluorooctanoate (PFOA) below 450 °C.

Infrared, Gas-phase spectra, FTIR, Spectral databa↗

DOE OSTI2022

SERDP PFAS 2.0 - An infrared spectral database for gas-phase quantitation of volatile per- and polyfluoroalkyl substances (PFAS)

Infrared, Gas-phase spectra, FTIR, Spectral databa↗

DOE OSTI2020

Spectral Variability of VHS J1256–1257b from 1 to 5 μm

Multiwavelength time-resolved observations of rotationally modulated variability from brown dwarfs and giant exoplanets are the most effective method for constraining their heterogeneous atmospheric structures. In a companion paper, we reported the discovery of strong near-infrared variability in HST/WFC3/G141 light curves of the very red L-dwarf companion VHS J1256–1257b. In this paper, we present a follow-up 36 hr Spitzer/IRAC Channel 2 light curve together with an in-depth analysis of the Hubble space telescope (HST) and the Spitzer data. The combined data set provides time-resolved light curves of VHS1256b sampling 1.1 to 4.5 μm. The Spitzer light curve is best fit with a single sine wave with a period of 22.04 ± 0.05 hr and a peak-to-peak amplitude of 5.76 ± 0.04%. Combining the period with a previously measured projected rotational velocity (v sin i), we find that VHS1256b is most consistent with equatorial viewing geometry. The HST/G141+Spitzer spectral energy distribution favors a model with a T{sub eff} of 1000 K and low surface gravity with disequilibrium chemistry. The spectral variability of VHS1256b is consistent with predictions from partly cloudy models, suggesting that heterogeneous clouds are the dominant source of the observed modulations. We find evidence at the 3.3σ level for amplitude variations within the 1.67 μm CH{sub 4} band, which is the first such detection for a variable L-dwarf. We compare the HST/G141 time-resolved spectra of three red L-dwarfs with high-amplitude near-infrared rotational modulations and find that although their time-averaged spectra are similar, their spectroscopic variabilities exhibit notable differences. This diversity reinforces the advantage of time-resolved spectroscopic observations for understanding the atmospheres of brown dwarfs and directly imaged exoplanets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Tuning Multigrid Methods with Robust Optimization and Local Fourier Analysis

Local Fourier analysis is a useful tool for predicting and analyzing the performance of many efficient algorithms for the solution of discretized PDEs, such as multigrid and domain decomposition methods. The crucial aspect of local Fourier analysis is that it can be used to minimize an estimate of the spectral radius of a stationary iteration, or the condition number of a preconditioned system, in terms of a symbol representation of the algorithm. In practice, this is a “minimax” problem, minimizing with respect to solver parameters the appropriate measure of work, which involves maximizing over the Fourier frequency. Often, several algorithmic parameters may be determined by local Fourier analysis in order to obtain efficient algorithms. Analytical solutions to minimax problems are rarely possible beyond simple problems; the status quo in local Fourier analysis involves grid sampling, which is prohibitively expensive in high dimensions. In this paper, we propose and explore optimization algorithms to solve these problems efficiently. Finally, several examples, with known and unknown analytical solutions, are presented to show the effectiveness of these approaches.

97 MATHEMATICS AND COMPUTING↗