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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Understanding Peelle’s Pertinent Puzzle bias in generalized least squares regression through eigenspectrum analysis

Certain correlation structures in the data covariance matrix (DCM) used for generalized least squares (GLS) regression can result in biased estimates, commonly known in the field of nuclear data evaluation as Peele’s Pertinent Puzzle (PPP). This article introduces a generative, forward modeling framework within which the PPP bias is characterized through an eigenspectrum analysis of the DCM. This analysis highlights the root cause of the bias, generalizes the problem beyond the nuclear data field, and provides insight to the problem regimes where it can occur. What follows is an understanding that the bias can show up for any experimental neutron time-of-flight data for which systematic uncertainties have been quantified. Lastly, a discussion of the adaptation of cross validation approaches that require pre-whitening to incorporate the known ‘fix’ to the PPP bias in the GLS estimator.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Emerging hierarchical dislocation structures: Insights from scanning electron microscopy-electron backscatter diffraction in situ tensile testing and multifractal analysis

Understanding the evolution of dislocation structures during plastic deformation is critical for predicting the mechanical performance of metallic materials. In this work, we applied in situ scanning electron microscopy/electron backscatter diffraction tensile testing combined with multifractal (MF) analysis to assess deformation-induced dislocation structure evolution in solution-annealed 304 L stainless steel, both in its as-received and neutron-irradiated states (5.4 displacements per atom). The analysis of kernel average misorientation patterns revealed the formation of hierarchical dislocation arrangements that exhibit clear MF scaling behavior. Despite pronounced visual differences between nonirradiated and irradiated specimens—most notably, the appearance of dislocation channels after irradiation—the singularity spectra suggest that both conditions give rise to similar underlying hierarchical structures. MF analysis provides a quantitative measure of the spatial complexity and self-organization of dislocation patterns, highlighting the accelerated emergence and evolution of the dislocation structures in irradiated polycrystalline materials, as well as the limitation of their spatial extent. The findings indicate that irradiation not only modifies microstructure but also alters correlation-driven dislocation organization. More generally, they demonstrate that MF analysis is a powerful tool for probing mesoscale deformation mechanisms.

Dislocation structures↗
DOE OSTI2025

Comparative Study of Solvatomorphs of Stryker's Reagent Using MicroED and Quantum Mechanics

Abstract The atomic position of hydrogen atoms in metal hydrides has been a long‐standing structural question in inorganic chemistry given that hydride delivery is integral to diverse chemical reactions. Microcrystal electron diffraction (microED), with it's increased sensitivity toward hydrogen atoms relative to X‐ray diffraction, offers a potential path to addressing this challenge. Herein, the first microED study of Stryker's reagent is reported, resulting in the structure of a new benzene solvate. Improved accuracy for hydrogen atom positions was obtained via a quantum crystallography (QCr) approach, Hirshfeld atom refinement (HAR). Structural and topological analysis supports edge bridging hydrides in the microED structure of a THF solvate form, consistent with previous diffraction studies. Interestingly, analysis of a new benzene solvate, discovered in this study, is consistent with mixed edge‐ and face‐bridging hydrides.

Jha, Kunal K. [Division of Chemistry &amp, Chemica↗
DOE OSTI2024

Agrivoltaic Racking Design Optimization Based on Wind and Snow Loading Finite Element Analysis

The racking structure of photovoltaic (PV) systems plays a critical role in ensuring the PV panels generate power properly as it provides integral structural support and sometimes even solar tracking ability. Distinct applications of PV systems require variations of racking structure designs, which yield different mechanical performances under external loading conditions from the environment, such as heavy wind and snow. Past works, such as Reddy et al. [1], have analyzed the pressure effects of wind and snow loading on the racking structure of conventional rooftop and utility PV systems, but there is little knowledge of how various agrivoltaic racking systems perform, in terms of stress and strain, under the same loading conditions. There is also a knowledge gap in the industry on a set of optimized agrivoltaic racking design standards. This study investigates the mechanical performance of various existing racking systems and proposes novel designs that are optimized for agrivoltaics applications under wind and snow loading.

Liao, Quanhuan↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin–Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56 Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37 Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeL x Cl y (L: organic ligand, x = 1–4, y = 0–3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Exploring the Genetic Basis of Wild Boar ( Sus scrofa ) and Its Connection to Classical Swine Fever Spread

Classical swine fever (CSF) is the one of the most devastating contagious diseases in domestic swine and wild boar/pigs (Sus scrofa). Population genetics is often used to estimate animal dispersal and can also help evaluate host population connectivity, which is crucial for understanding pathogen dispersal. We surveyed genetic population structure of boars using MIG-seq analysis to clarify the geographic barriers that influence boar dispersal in north-central Japan and to demonstrate the relationship between the spread of CSF infection among boars and their population structure. We obtained 382 single-nucleotide polymorphisms from 348 wild boar samples, and the results of STRUCTURE analysis indicated that the highest ΔK value was at K = 2, followed by K = 4. Based on these results, it is evident that the Abukuma river, a major river within north-central Japan, does not act as a barrier to the gene flow of boars, but rather that human infrastructure hinders their dispersal. Further, according to the time series change in the capture site of CSF-infected wild boar and the sum of the probability of belonging to each of the four clades in individual CSF-infected wild boar, our results indicated that the genetic structure of boar populations was correlated with the outbreak pathway of CSF across our study region. Our study suggests that predictions of disease spread, especially for widely distributed host species, is challenging because of the risk of cryptic breaks and changes in wide range connectivity; however, understanding the genetic population structure of wild boar can be a useful tool for predicting the spread of CSF. We concluded that genetic analysis of host population structure may have the possibility to improve predictions of the future dynamics of disease spread.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Co 3 Ga 2 Ge 5 : Probing site mixing of the Ru 3 Sn 7 structure type with elements difficult to distinguish by diffraction

Co 3 Ga 2 Ge 5 was synthesized through arc-melting stoichiometric ratios of the elements, and a Ru 3 Sn 7 -type structure was confirmed by X-ray diffraction. Because Co 3 Ga 2 Ge 5 contains Ga and Ge, which have very similar X-ray and neutron scattering factors, any Ga/Ge crystallographic site preference cannot be determined with diffraction alone. The purpose of this study is to highlight the importance of using multiple techniques to characterize otherwise structurally ambiguous intermetallic compounds. Here, we utilize 71 Ga nuclear magnetic resonance spectroscopy and an analysis of the X-ray absorption fine structure to clarify the amount of Ga/Ge site mixing. Our combined use of X-ray diffraction and spectroscopy provides a comprehensive structural analysis of Ga site mixing across the Ge crystallographic sites, enhancing the understanding of the structure and properties of Co 3 Ga 2 Ge 5 .

36 MATERIALS SCIENCE↗
DOE OSTI2026

Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories

Non-targeted liquid chromatography tandem highresolution mass spectrometry (LC−MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC−MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and featurebased molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC−MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.

DOM↗
DOE OSTI2024

Impacts of Irradiation Structural Behavior on Thermal Hydraulics Safety Analysis to Support MURR LEU Conversion

The University of Missouri Research Reactor (MURR) located in Columbia, Missouri is one of six U.S. High Performance Research Reactors (USHPRR), including one critical facility, that is actively collaborating with U. S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) Material Management and Minimization (M3) Office of Reactor Conversion and Uranium Supply to convert from the use of highly enriched uranium (HEU; ≥20 wt% U-235) to low-enriched uranium (LEU; <20 wt% U-235) fuel. A new type of very high-density LEU fuel based on an alloy of uranium and 10 wt% molybdenum (U-10Mo) is expected to allow the conversion to LEU of MURR, as well as four other USHPRR. MURR has been working with the Reactor Conversion Pillar at Argonne to perform fuel element design and fuel cycle performance analyses, steady-state thermal hydraulics safety analysis, and accident safety analyses in preparation for the conversion of MURR and to support a preliminary safety analysis report for conversion to LEU fuels. Subsequent analyses have also been performed, including transition cycles where all-fresh LEU fuel elements are introduced upon conversion and progressing through reactor operations the core is brought to equilibrium. Thermal hydraulics safety analyses performed as part of the above have employed an assumption on channel gap reduction due to burnup-related phenomena including fuel swelling, irradiation creep, and oxide layer buildup. Recently, a series of structural analyses have been performed on the MURR LEU fuel plates and an element due to significant differences between the plate and element designs of the MURR HEU and LEU fuels. In addition, NUREG-1537 indicates that structural phenomena are to be evaluated. Two separate types of structural analyses were performed for the MURR LEU fuel element: fluid-structure interaction (FSI) and irradiation thermo-mechanical. The FSI analysis evaluated the effects of hydraulic forces on the MURR LEU fuel element to quantify the flow-induced plate deflection, and a minimal impact to the channel gap thickness was predicted under prototypic and bounding conditions. The irradiation thermo-mechanical analysis evaluated the effects of fuel swelling, irradiation creep, and thermal expansion for the MURR LEU plates and the element for prototypic thermal and irradiation conditions based on a high-fidelity approach multiphysics approach. Overall, this thermo-mechanical analysis predicts smaller gap thickness changes in previously limiting regions. Larger changes are predicted in the middle of channels, and for end channels where power density is not typically a maximum. An additional thermo-mechanical analysis was performed for the outermost HEU fuel plate, which showed a similar magnitude of deflection as the outermost LEU plate. Due to substantial differences between the channel gap reductions assumed for the previous safety analyses and those predicted by the irradiation thermo-mechanical analysis, a need to evaluate their impact on the thermal hydraulics safety analyses arose. This report presents the results from the steady-state safety analyses for normal operation as well as the accident analyses for the two most limiting accident scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Noncanonical folding of peptoid oligomers: Formation of a closed conformation in nonpolar solvent

Peptoids provide a versatile platform for foldamer design, yet their conformational behavior in low-dielectric media remains poorly understood. The structural characteristics of N-1-phenylethylglycine (Nspe) homo-oligomers were inves-tigated in chloroform, a solvent that mimics the interior of lipid bilayers, using nuclear magnetic resonance (NMR) spec-troscopy and molecular dynamics (MD) simulations. Nspe7 populated two distinct compact conformations, while Nspe10 adopted a single homogeneous conformation related to the previously reported Nspe9 threaded-loop structure. Integrated experimental and computational analysis reveals that these structures are stabilized by cooperative end-to-end intramo-lecular hydrogen bonding, cis-trans backbone isomerism, and hydrophobic side-chain shielding. The resulting structures minimize exposed polar surface area, demonstrating a closed conformation in the low-dielectric environment. These find-ings establish specific chain-length requirements for achieving well-defined closed conformations. This work provides insights into peptoid folding in nonpolar media, enabling rational design strategies for solvent-directed conformational switching systems.

Oh, Jinyoung↗
DOE OSTI2025

Machine Learning Analysis of Temperature-Strain Relationships for Structural Health Monitoring of Pipes: Self-powered wireless sensor system for health monitoring of liquid-sodium cooled fast reactors

This report presents machine learning (ML) analysis of temperature-strain relationships for structural health monitoring of nuclear reactor stainless steel (SS) pipes with the strain gauge sensor directly printed on the pipe with a 3D conformal aerosol jet printer. We investigate correlations for two sensor pairs installed on the same SS304 pipe: commercial K-type thermocouple with a printed gold strain gauge (TC3-SG3), and commercial K-type thermocouple with commercial Kyowa strain gauge (TC0-SG0). The temperature ranges for the sensor pairs TC0-SG0 and TC3-SG3 are 20.00°C to 266.37°C and 39.95°C to 219.28°C respectively. ML algorithms in this study include Linear Regression (baseline method), Ridge Regression, Lasso Regression, and Gradient Boosting. Performance evaluation metrics include Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), R 2 Score, and Explained Variance. Using advanced feature engineering techniques, we extracted 27 temperature-based features and 30 strategic inclusion features. The best performance was obtained with the Gradient Boosting method, which achieves prediction accuracy of R 2 = 0.9999 and RMSE = 7.69 μStrain for TC0-SG0, and R 2 = 0.9998 and RMSE = 18.03 μStrain for TC3-SG3. While the temperature-strain correlations are weaker for the gauge directly printed on the pipe than for the commercial strain gauge, deployment-ready performance exceeding industry standards is achieved for both sensor pairs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Structural dynamics of the renewable energy economy: A longitudinal input-output insights for a resilient transition

As countries accelerate their energy transitions, understanding how renewable energy (RE) systems structurally integrate into national economies is essential. This study presents a longitudinal economic input-output (EIO) analysis of the renewable energy sector in South Korea from 2016 to 2022. We develop a novel EIO-based framework that disaggregates the RE sector both by energy source (thermal, hydro, nuclear and renewable) and by industrial function (manufacturing, generation, and services), allowing for a detailed assessment of production dynamics, value-added creation, and import dependency. By quantifying backward and forward linkages and induced economic effects, the analysis reveals persistent structural vulnerabilities in renewable manufacturing and increasing sectoral interdependencies. Results reveal that while the renewable energy sector's production and value-added shares have increased, critical segments remain highly import-dependent, particularly in equipment manufacturing. The analysis highlights systemic gaps in domestic supply chain resilience and offers sector-specific insights for reducing vulnerability and enhancing energy security. Although applied to South Korea as a case study, the proposed framework is designed to be transferable to other national contexts where renewable energy planning requires economic structural insights. The findings offer policy-relevant guidance for enhancing domestic energy resilience and aligning industrial strategy with long-term decarbonization goals.

Economic linkage↗
DOE OSTI2025

Cross-species analysis of FcγRIIa/b (CD32a/b) polymorphisms at position 131: structural and functional insights into the mechanism of IgG- mediated phagocytosis in human and macaque

Introduction Antibodies play a critical role in immunity in part by mediating clearance of pathogens and infected cells by antibody-dependent cellular phagocytosis (ADCP) through engagement of Fc gamma receptors (FcγRs) on innate immune cells. Among these, FcγRIIa (CD32a) is a key activating receptor expressed on macrophages, dendritic cells, and other antigen-presenting cells. Its affinity for IgG and ability to mediate ADCP is influenced by allelic polymorphisms. In humans, a single amino acid polymorphism at position 131, where histidine (H) is substituted with arginine (R), leads to decreased IgG1 and IgG2 subclass binding affinity and, consequently, lower efficiency of phagocytic responses. Rhesus macaques ( Macaca mulatta ), which are widely used as nonhuman primate models, exhibit a similar polymorphism at position 131 of FcγRIIa, but with arginine replaced by proline (P). Here, we investigated structure-function relationships associated with the FcγRIIa polymorphism at position 131 in both species, specifically with respect to IgG1 and IgG2. Methods We determined the structures of complexes formed by each variant with IgG1 Fc and those formed by the higher affinity variant with IgG2 Fc for both species by x-ray crystallography and linked these structures to affinity and activity using SPR and an ADCP assay. We also determined the structure of human inhibitory FcγRIIb (CD32b) in complex with IgG1 Fc by x-ray crystallography. Results Through analysis of these structures, our studies reveal that FcγRIIa engagement is minimally influenced by Fc glycan composition, distinguishing it from FcγRIIIa whose affinity is strongly influenced by glycan-composition. Comparative structures of human and macaque FcγRIIa variants demonstrate species- and allele-specific differences in Fc binding, but our functional assays showed only minimal allele-specific effects in humans. In contrast, allele-specific effects in macaques were highly significant; the macaque P 131 variant showing uniformly reduced IgG affinity. Conclusion These insights highlight fundamental interspecies and allelic distinctions that are critical for interpreting FcγRIIa-mediated effector functions in macaque models and for optimizing translational antibody and vaccine design.

Tolbert, William D.↗
DOE OSTI2026

Exploring Resonance Structures in the Partial-Wave Analysis of ¿p0 Photoproduction at GlueX

This thesis studies what happens when a photon (¿) collides with a proton (p) and produces a neutral omega (¿) and pion (p0) pair, with a recoiling proton (p'), expressed as ¿p ¿ ¿p0p'. We study this and other reactions to better understand the strong nuclear force; one of the four fundamental forces that govern all the physics of the universe. This force is specifically responsible for the binding and decay of subatomic particles, such as the ones here. While we understand the ¿ and p0, what we are actually interested in is a short-lived unknown particle X that decays via X ¿ ¿p0. There are a multitude of possible particles X can be, and so our focus in this work is to find out what X is by determining its properties from the particles we measure. We do this via an intricate analysis procedure known as “partial-wave analysis”. By analogy, one can think of our particle detector as a buoy, and the particles we want to analyze (X) as pebbles hitting a pond. The waves created by the pebble will move our buoy, giving us information about the pebble that produced the wave. However, when multiple pebbles hit our pond, the waves overlap and interfere with each other. Our buoy only can measure the complicated interfering result of all the waves. To disentangle this, our partial-wave analysis works by modeling this interference pattern so that we may infer the properties of the pebbles (particles) we produced. In this thesis, we review the relevant experimental history in photoproduction and related production mechanisms, as well as the theoretical foundations that motivate our measurement. We describe the methods used to collect our data at the GlueX experiment stationed at the Jefferson Lab accelerator facility in Newport News, Virginia. We then detail the selections we apply to ensure our events are almost exclusively ¿p ¿ ¿p0p'. We cover the intensity model, how we verify its capabilities, and finally present our results together with systematic studies. Our primary result is the detection of a b1(1235) meson interfering with a wide JPC = 1-- vector state, measured via a mass-independent partial-wave analysis. It provides precise experimental results that can be used as input for theoretical models of the reaction, yielding conclusions about the procedures responsible for how our universe behaves at its most basic level.

Scheuer, Kevin [College of William and Mary, Willi↗