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DOE OSTI2021

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach .

Despite widespread use of factor fixing in environmental modeling, its effect on model predictions has received little attention and is instead commonly presumed to be negligible. We propose a proof-of-concept adaptive method for systematically investigating the impact of factor fixing. The method uses Global Sensitivity Analysis methods to identify groups of sensitive parameters, then quantifies which groups can be safely fixed at nominal values without exceeding a maximum acceptable error, demonstrated using the 21-dimensional Sobol’ G-function. Furthermore, three error measures are considered for quantities of interest, namely Relative Mean Absolute Error, Pearson Product-Moment Correlation and Relative Variance. Results demonstrate that factor fixing may cause large errors in the model results unexpectedly, when preliminary analysis suggests otherwise, and that the default value selected affects the number of factors to fix. To improve the applicability and methodological development of factor fixing, a new research agenda encompassing five opportunities is discussed for further attention.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Empirical probability and machine learning analysis of m , n = 2, 1 tearing mode onset parameter dependence in DIII-D H-mode scenarios

m, n = 2, 1 tearing mode onset empirical probability and machine learning analyses of a multiscenario DIII-D database of over 14 000 H-mode discharges show that the normalized plasma beta, the rotation profile, and the magnetic equilibrium shape have the strongest impact on the 2,1 tearing mode stability, in qualitative agreement with neoclassical tearing modes (m and n are the poloidal and toroidal mode numbers, respectively). In addition, 2,1 tearing modes are most likely to destabilize when n > 1 tearing modes are already present in the core plasma. The covariance matrix of tearing sensitive plasma parameters takes a nearly block-diagonal form, with the blocks incorporating thermodynamic, current and safety factor profile, separatrix shape, and plasma flow parameters, respectively. This suggests a number of paths to improved stability at fixed pressure and edge safety factor primarily by preserving a minimum of 1 kHz differential rotation, increasing the minimum safety factor above unity, using upper single null magnetic configuration, and reducing the core impurity radiation. In addition, lower triangularity, lower elongation, and lower pedestal pressure may also help to improve stability. Furthermore, the electron and ion temperature, collisionality, resistivity, internal inductance, and the parallel current gradient appear to only weakly correlate with the 2,1 tearing mode onsets in this database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Combination of cluster number counts and two-point correlations: validation on mock Dark Energy Survey

ABSTRACT We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy–cluster cross-correlations, cluster autocorrelations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year 1 (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES-Y1 experiment. We compare the χ2 values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of DES-Y1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalogue level simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Systematic Uncertainties of the NOvA Neutrino Oscillation Analysis

NOvA is a two detector long-baseline neutrino oscillation experiment using Fermilab’s 700 kW NuMI muon neutrino beam. It studies the disappearance of muon (anti)neutrinos and the appearance of electron (anti)neutrinos in the beam over a distance of 810 km between the detectors. The observations can be used to constrain neutrino oscillation parameters: $\theta_{23}, \Delta m_{32}^2$ and $\delta_{CP}$ in the standard 3 active neutrinos paradigm. Although statistical uncertainties currently dominate in these measurements, understanding the major sources of systematic uncertainties and their correlations is vital for both the interpretation and precision of the results and also for further improvements of the analysis. This note presents details of the systematic uncertainties affecting NOvA's neutrino oscillation analysis results and how we evaluate them by generating systematically shifted predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Evaluating the Utility of Permethylated Polysaccharide Solution NMR Data for Characterization of Insoluble Plant Cell Wall Polysaccharides

Plant cell wall polysaccharide analysis encompasses the utilization of a variety of analytical tools, including gas and liquid chromatography, mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy. These methods provide complementary data, which enable confident structural proposals of the many complex polysaccharide structures that exist in the complex matrices of plant cell walls. However, cell walls contain fractions of varying solubilities, and a few techniques are available that can analyze all fractions simultaneously. We have discovered that permethylation affords the complete dissolution of both soluble and insoluble polysaccharide fractions of plant cell walls in organic solvents such as chloroform or acetonitrile, which can then be analyzed by a number of analytical techniques including MS and NMR. In this work, NMR structure analysis of 10 permethylated polysaccharide standards was undertaken to generate chemical shift data providing insights into spectral changes that result from permethylation of polysaccharide residues. This information is of especial relevance to the structure analysis of insoluble polysaccharide materials that otherwise are not easily investigated by solution-state NMR methodologies. The preassigned NMR chemical shift data is shown to be vital for NMR structure analysis of minor polysaccharide components of plant cell walls that are particularly difficult to assign by NMR correlation data alone. With the assigned chemical shift data, we analyzed the permethylated samples of destarched, alcohol-insoluble residues of switchgrass and poplar by two-dimensional NMR spectral profiling. Thus, we identified, in addition to the major polysaccharide components, two minor polysaccharides, namely, <5% 3-linked arabinoxylan (switchgrass) and <2% glucomannan (poplar). In particular, the position of the arabinose residue in the arabinoxylan of the switchgrass sample was confidently assigned based on chemical shift values, which are highly sensitive to local chemical environments. Furthermore, the high resolution afforded by the 1H NMR spectra of the permethylated switchgrass and poplar samples allowed facile relative quantitative analysis of their polysaccharide composition, utilizing only a few milligrams of the cell wall material. As a result, the concepts herein developed will thus facilitate NMR structure analysis of insoluble plant cell wall polysaccharides, more so of minor cell wall components that are especially challenging to analyze with current methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

High-Resolution X-ray Spectromicroscopy Reveals Process-Structure Correlations in sub-5-μm Diameter Carbon Nanotube-Polymer Composite Dry-Spun Yarns

A persistent lack of detailed and quantitative structural analysis of these hierarchical carbon nanotube (CNT) ensembles precludes establishing processing-structure-property relationships that are essential to enhance macroscale performance (e.g., in mechanical, electrical, thermal applications). Here, in this work, we use scanning transmission X-ray microscopy (STXM) to analyze the hierarchical, twisted morphology of dry-spun CNT yarns and their composites, quantifying key structural characteristics such as density, porosity, alignment, and polymer loading. As the yarn twist density increases (15,000 to 150,000 turns per meter), the yarn diameter decreased (4.4–1.4 μm) and density increased (0.55–1.26 g·cm –3 ), as intuitively expected. Yarn density, ρ, ubiquitously scaled with diameter d according to ρ ~ d –2 for all parameters studied here. Spectromicroscopy probes with 30 nm resolution and elemental specificity were employed to analyze the radial and longitudinal distribution of the oxygen-containing polymer content (~30% weight fraction), demonstrating nearly perfect filling of the voids between CNTs with a vapor-phase polymer coating and cross-linking process. These quantitative correlations highlight the intimate connections between processing conditions and yarn structure with important implications for translating the nanoscale properties of CNTs to the macroscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Molecular orbital symmetry-driven trimer formation in Kagome correlated electron materials

Correlated electron materials with molecular orbital states extending over transition metal clusters can host multiferroicity, spin frustration, and unconventional insulating phases. However, the fundamental criteria that govern cluster formation and stability remain unclear. Here, we identify a symmetry, correlation, and electron-filling-driven criteria that stabilize triangular metal trimers in materials displaying transition metal Kagome patterns. Using density functional theory and chemical bonding analysis, we show that trimer formation emerges when 6–8 electrons occupy molecular orbitals derived from transition metal d-states, achieving near-complete filling of bonding states while avoiding antibonding occupation, and correlations are of intermediate strength. This principle explains the stability of Nb 3 X 8 (X = Cl, Br, I), and more broadly, our findings offer a general design rule to obtain quantum materials with quantum states extended across transition metal clusters.

Kumari, Varsha [Indiana Univ., Bloomington, IN (Un↗

DOE OSTI2021

A study of DC electrical breakdown in liquid helium through analysis of the empirical breakdown field distributions

Here we report results from a study on electrical breakdown in liquid helium using near-uniform-field stainless steel electrodes with a stressed area of ~0.7cm 2 . The distribution of the breakdown field is obtained for temperatures between 1.7 K and 4.0 K, pressures between the saturated vapor pressure and 626 Torr, and with electrodes of different surface polishes. A data-based approach for determining the electrode-surface-area scaling of the breakdown field is presented. The dependence of the breakdown probability on the field strength as extracted from the breakdown field distribution data is used to show that breakdown is a surface phenomenon closely correlated with Fowler–Nordheim field emission from asperities on the cathode. We show that the results from this analysis provide an explanation for the supposed electrode gap-size effect and also allow for a determination of the breakdown-field distribution for arbitrary shaped electrodes. Most importantly, the analysis method presented in this work can be extended to other noble liquids to explore the dependencies for electrical breakdown in those media.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements

Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and resolving their structures at subnanometre resolution [Tegunov et al. (2021)View full citation, Nat. Methods, 18, 186–193]. Despite improvements in data quality as a result of advances in detector technology, microscope stability and stage precision, the analysis and interpretation of tomograms remains challenging due to a low signal-to-noise ratio and reconstruction artifacts stemming from experimental constraints in specimen tilt during data collection resulting in a missing wedge in the Fourier space. Recently, self-supervised deep-learning methods have been proposed for contrast enhancement and reduction of resolution anisotropy in reconstructed tomograms. Here, we evaluate several state-of-the-art deep-learning methods which aim to improve the interpretability of cryo-ET reconstructions, with a focus on their performance on downstream tasks of template matching, subtomogram averaging and segmentation. We propose new training architectures and a loss function based on Fourier shell correlation that show improved performance over the standard U-Net with L1/L2 losses. We demonstrate our analysis on four diverse experimental datasets: purified 80S ribosomes, in situ Chlamydomonas reinhardtii, immature HIV-1 virus-like particles and INS-1E cells.

contrast enhancement↗

DOE OSTI2022

Numerical Analysis of Blast Effects and Mitigation in the Far-Field from Small Explosions

Requirements for explosive safety are often given in terms of a “K-Factor”, correlating incident blast effects with the distance and TNT equivalent weight of an explosion. Traditionally, this is conducted using empirical correlations to experimental measurements (e.g., the Kingery–Bulmash equations). In the far-field, empirical verification of incident overpressure and impulse magnitudes can be difficult; extrapolations from data give expected values at reasonable standoff distances that sometimes are too small to measure on available equipment but are larger than some regulations require. The present paper describes the results of numerical hydrocode analysis to verify the expected incident overpressure and impulse from small hemispherical ground charges of TNT at these relatively large distances. Furthermore, the dynamic effect of incident blast waves on lightweight, modular mitigation barriers is studied to gauge their effectiveness at providing safety standard compliance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Charge radii in covariant density functional theory: A global view

A systematic global investigation of differential charge radii has been performed within the CDFT framework for the first time. Theoretical results obtained with conventional covariant energy density functionals and the separable pairing interaction of Tian et al. [Phys. Lett. B 676, 44 (2009)] are compared with experimental differential charge radii in the regions of the nuclear chart in which available experimental data crosses the neutron shell closures at N=28,50,82, and 126. The analysis of absolute differential radii of different isotopic chains and their relative properties indicate clearly that such properties are reasonably well described in model calculations in the cases when the mean-field approximation is justified. However, while the observed clusterization of differential charge radii of different isotopic chains is well described above the N=50 and N=126 shell closures, it is more difficult to reproduce it above the N=28 and N=82 shell closures because of possible deficiencies in the underlying single-particle structure. The impact of the latter has been evaluated for spherical shapes and it was shown that the relative energies of the single-particle states and the patterns of their occupation with increasing neutron number have an appreciable impact on the evolution of the δ$\langle$r2$\rangle$N,N' values. These factors also limit the predictive power of model calculations in the regions of high densities of the single-particle states of different origin. It is shown that the kinks in the charge radii at neutron shell closures are due to the underlying single-particle structure and due to weakening or collapse of pairing at these closures. The regions of the nuclear chart in which the correlations beyond mean field are expected to have an impact on charge radii are indicated; the analysis shows that the assignment of a calculated excited prolate minimum to the experimental ground state allows us to understand the trends of the evolution of differential charge radii with neutron number in many cases of shape coexistence even at the mean-field level. It is usually assumed that pairing is a dominant contributor to odd-even staggering (OES) in charge radii. Our analysis paints a more complicated picture. It suggests a new mechanism in which the fragmentation of the single-particle content of the ground state in odd-mass nuclei due to particle-vibration coupling provides a significant contribution to OES in charge radii.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

A Meta-Analysis of Stressors from the Total Environment Associated with Children’s General Cognitive Ability

General cognitive ability, often referred to as ‘general intelligence’, comprises a variety of correlated abilities. Childhood general cognitive ability is a well-studied area of research and can be used to predict social outcomes and perceived success. Early life stage (e.g., prenatal, postnatal, toddler) exposures to stressors (i.e., chemical and non-chemical stressors from the total (built, natural, social) environment) can impact the development of childhood cognitive ability. Building from our systematic scoping review (Ruiz et al., 2016), we conducted a meta-analysis to evaluate more than 100 stressors related to cognitive development. Our meta-analysis identified 23 stressors with a significant increase in their likelihood to influence childhood cognitive ability by 10% or more, and 80 stressors were observed to have a statistically significant effect on cognitive ability. Stressors most impactful to cognition during the prenatal period were related to maternal health and the mother’s ability to access information relevant to a healthy pregnancy (e.g., diet, lifestyle). Stressors most impactful to cognition during the early childhood period were dietary nutrients (infancy), quality of social interaction (toddler), and exposure to toxic substances (throughout early childhood). In conducting this analysis, we examined the relative impact of real-world exposures on cognitive development to attempt to understand the inter-relationships between exposures to both chemical and non-chemical stressors and early developmental life stages. Our findings suggest that the stressors observed to be the most influential to childhood cognitive ability are not permanent and can be broadly categorized as activities/behaviors which can be modified to improve childhood cognition. This meta-analysis supports the idea that there are complex relationships between a child’s total environment and early cognitive development.

Nilsen, Frances M. (ORCID:0000000241936951)↗

DOE OSTI2024

Superoxide Radicals in Uranyl Peroxide Solids: Lasting Signatures Identified by Electron Paramagnetic Resonance Spectroscopy

Abstract U(VI) peroxide phases (studtite and meta‐studtite) are found throughout the nuclear fuel cycle and exist as corrosion products in high radiation fields. Peroxides are part of a family of reactive oxygen species (ROS) that include hydroperoxyl and superoxide species and are produced during alpha radiolysis of water. While U(VI) peroxides have been thoroughly investigated, the incorporation and stability of ROS species within studtite have not been validated. In the current study, electron paramagnetic resonance (EPR) spectroscopy was used to identify the presence of free radicals within a series of U(VI) peroxide samples containing depleted, highly enriched, and natural uranium. Density functional theory calculations indicated that the predicted EPR signals matched well with a superoxide (O 2 − ⋅) species incorporated into the studtite structure, confirming the presence of ROS in the material. Further analysis of samples that were synthesized between 1945 and 2023 indicated that there is a correlation between the radical signal and the product of specific activity multiplied by age of the sample.

Scherrer, Sarah K.↗

DOE OSTI2024

Superoxide Radicals in Uranyl Peroxide Solids: Lasting Signatures Identified by Electron Paramagnetic Resonance Spectroscopy

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Achieving Multimodal and Multicolor Luminescence in LaAlO 3 :Pr 3+ , Gd 3+ via Trap Engineering and Energy Transfer

Achieving multimodal luminescence within a single phosphor is vital for multifunctional applications but remains challenging due to complex color tuning and trap engineering. In this study, we report Pr 3+ and Gd 3+ co‐doped LaAlO 3 (LAO:PG) phosphors, designed through careful modulation of multilevel traps and Pr 3+ → Gd 3+ energy transfer dynamics. These materials exhibit diverse luminescence modes, including down‐conversion luminescence (DCL), up‐conversion luminescence (UCL), persistent luminescence (PersL), optically stimulated luminescence (OSL), and thermally stimulated luminescence (TSL) across a wide spectral range. Unlike previously studied Pr 3+ ‐doped LAO, the co‐doped LAO:PG shows DCL in both UV‐visible and NIR regions and displays ultraviolet‐C UCL under visible excitation. Notably, we observe, for the first time, PersL lasting several minutes in these phosphors—an improvement over the non‐PersL behavior of Pr 3+ ‐only doped LAO. Additionally, the LAO:PG phosphors exhibit strong OSL response. TSL analysis reveals five distinct trap levels linked to these properties. Density functional theory calculations further correlate intrinsic defects to these traps, supporting a proposed mechanism for the observed multimodal luminescence. These findings highlight LAO:PG as a promising platform for developing advanced phosphors with integrated luminescence modes, paving the way for future applications in data storage, phototherapy, and anti‐counterfeiting technologies.

Chemistry↗

DOE OSTI2022

Combined DSC/TGA Analysis to Rationalize the As-Sintered Microstructure in Binder Jet Additively Manufactured H13 Tool Steel

In this study, a combined DSC/TGA analysis has been used to rationalize the constant liquid channel width at higher sintering temperatures in H13 tool steel manufactured by binder jet additive manufacturing. Using enthalpy conservation along with coupled DSC + TGA analysis, the phase fractions of liquid and austenite phases during heating and cooling respectively were estimated. The estimates from the DSC/TGA analysis were compared with equilibrium phase fraction predictions from ThermoCalc. The resulting phase evolution was correlated to the as-sintered microstructure to rationalize the unexpected lack of change in liquid channel width at higher sintering temperatures.

Kannan, Rangasayee↗

DOE OSTI2020

Insights from machine learning of carbon electrodes for electric double layer capacitors

Recent years have witnessed the broad use of carbon electrodes for electric double layer capacitors (EDLCs) because of large surface area, high porosity and low cost. Whereas experimental investigations are mostly focused on the device performance, computational studies have been rarely concerned with electrochemical properties at conditions remote from equilibrium, limiting their direct applications to materials design. Through a comprehensive analysis of extensive experimental data with various machine-learning methods, we report herein quantitative correlations between the structural features of carbon electrodes and the in-operando behavior of EDLCs including energy and power density. Machine learning allows us to identify important characteristics of activated carbons useful to optimize their efficiency in energy storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗