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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Adaptive Reference Scheme to Extend the Functional Range of Optical Backscatter Reflectometry in Extreme Environments

Optical frequency domain reflectometry (OFDR) measurements are performed by recording the interference pattern of light backscattered by density fluctuations or Bragg gratings along the length of an optical fiber. Changes in local temperature or strain in the fiber cause shifts in the backscattered light spectrum, which can be calibrated to the applied temperature or strain. Comparing the backscattered optical spectra from each reflection site with a reference (unstrained) spectrum allows for quantification of local spectral shifts. While mature OFDR-based technologies can provide high-precision spatially distributed measurements over kilometer lengths, the post-processing approaches used to recover spectral shifts from raw optical intensity data can limit the use of OFDR in harsh environments. This paper presents a novel approach to post-processing OFDR data which extends the usability of optical fibers exposed to harsh environments. This approach is the first to use the spectral shift quality, calculated for two OFDR measurements, to identify an appropriate reference scan from which the spectral shift can be determined. Here, three experimental data sets are used to test the algorithm: (1) an optical fiber heated to >950°C, (2) an aluminum-embedded optical fiber under strain from differential thermal expansion, and (3) an optical fiber exposed to a total neutron flux of 2×10 13 n/cm 2 /s over 40 hours in a nuclear test reactor. Results show that using a metric of quality to select an appropriate reference measurement extends the functional range of OFDR strain and temperature sensors. Furthermore, this algorithm can be applied to existing OFDR data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

DOE new players Carbon cycle (2016-2021)

The overarching scientific goals of our multidisciplinary grant was to further expand understanding about the key microorganisms (players), metabolic strategies (processes), and interspecies relationships (interactions) involved in the formation and oxidation of methane in the environment. This research applied novel environmental metagenomics, transcriptomics, and proteomic techniques, state-of-the-art analytical imaging, stable isotope geochemistry, and reaction-transport modeling to address these goals and develop an ‘ecosystems level’ understanding of the factors which regulate microbial methane cycling in anoxic sedimentary ecosystems. For decades, it was believed that the obligate step in methanogenesis catalyzed by methyl coenzyme M reductase (Mcr) was limited to a specific branch of the archaeal Domain, formerly known as the Euryarchaeota. Less than a decade ago co-I Tyson’s team published a surprising metagenomic-based discovery of divergent Mcr genes in a novel uncultured phylum (Bathyarchaeota), catalyzing a major shift in thinking about the diversity of microorganisms that encode the potential for methane (or higher alkane) metabolism in anoxic environments (Evans et al., 2015). In our work here, we further expand on the groups of archaea harboring the genomic potential for methane or hydrocarbon metabolism using environmental metagenomics and new gene targeted bioinformatics techniques. We additionally advanced understanding about the terminal electron acceptors and metabolic potential supporting the anaerobic oxidation of methane (AOM) in terrestrial ecosystems, specifically focused on new lineages of ANME archaea capable of respiring manganese oxides with methane presumably using large extracellular multi-heme cytochrome complexes. New details about specific syntrophic mechanisms underlying the exchange of electrons during sulfate-coupled methane oxidation between ANME-2 archaea and their sulfate-reducing bacterial partners were also elucidated as part of this funded project. Through a series of experimental ‘omics and single cell stable isotope probing studies with incubated environmental sediment samples and a cultured model electrogenic microorganism combined with model-based predictions. Combined, this work provides strong support for the hypothesis of direct interspecies electron transfer (DIET) is the dominant syntrophic mechanism controlling the anaerobic oxidation of methane with sulfate over other proposed mechanisms and additionally illustrates important spatial constraints and the underlying physico-chemical factors influencing AOM syntrophic consortia structure for DIET and extracellular metal respiration. This collaborative multi-institutional project successfully advanced several of our milestone goals including the identification of new microbial players containing methyl coenzyme M reductases hypothesized to be central to methane or hydrocarbon cycling in anoxic environments and enhancing fundamental knowledge about the role extracellular electron transfer plays in the ecophysiology of methanotrophic archaea respiring metal oxides and in the physical and metabolic structuring of syntrophic interactions in methane-rich sedimentary ecosystems.

03 NATURAL GAS↗
DOE OSTI2024

Origin of spin-driven ferroelectricity and effect of external pressure on the complex magnetism of the 6⁢ H perovskite Ba 3 ⁢Ho⁢Ru 2⁢ O 9

The compound Ba 3 HoRu 2 O 9 magnetically orders at 50 K (T N1 ), followed by another complex magnetic ordering at 10.2 K (T N2 ). The second magnetic phase transition was characterized by the coexistence of two competing magnetic ground states associated with two different magnetic wave vectors (K 1 = 0.5 0 0 and K 2 = 0.25 0.25 0). The multiferroicity and magnetoelectric coupling were predicted below T N2 in these 4d-based materials. Here, in this work, we have discussed the origin of spin-driven ferroelectricity, which is not known yet, and the nature of magnetoelectric domains. We have investigated the compound through time-of-flight neutron diffraction, synchrotron x-ray diffraction (XRD), ac susceptibility, frequency-dependent complex dielectric spectroscopy, and dc magnetization under external pressure. We have demonstrated that the noncollinear structure involving two different magnetic ions, Ru (4d) and Ho (4f), breaks the spatial inversion symmetry via inverse Dzyaloshinskii-Moriya (DM) interaction through strong 4d-4f magnetic correlation, which shifts the oxygen atoms and results in nonzero polarization. Such an observation of inverse DM interaction from two different magnetic ions which cause ferroelectricity is rarely observed. The stronger spin-orbit coupling of 4d orbital might play a major role in creating DM interaction of noncollinear spins. We have systematically studied the spin and dipolar dynamics, which exhibit intriguing behavior with shorter coherence lengths of second magnetic phase associated with the k 2 wave vector. The results manifest the development of finite-size magnetoelectric domains instead of true long-range ordering, which justifies the experimentally obtained low value of ferroelectric polarization. The lattice parameters and volume show a sharp anomaly at T N2 obtained by analyzing the temperature-dependence XRD, which is consistent with the ferroelectric transition, predicting a noncentrosymmetric space group, $P\bar{6}2c$, for this compound. Furthermore, we have investigated the effect of external pressure on this complex magnetism. The result reveals an enhancement of ordering temperature by the application of external pressure (~1.6 K/GPa). The external pressure might favor stabilizing the magnetic ground state associated with second magnetic phase. Our study shows an unconventional mechanism of spin-driven ferroelectricity involving inverse DM interaction between Ru (4d) and Ho (4f) magnetic ions due to strong 4d-4f cross coupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Selective One-Dimensional 13 C– 13 C Spin-Diffusion Solid-State Nuclear Magnetic Resonance Methods to Probe Spatial Arrangements in Biopolymers Including Plant Cell Walls, Peptides, and Spider Silk

Two-dimensional (2D) and 3D through-space 13 C– 13 C homonuclear spin-diffusion techniques are powerful solid-state nuclear magnetic resonance (NMR) tools for extracting structural information from 13 C-enriched biomolecules, but necessarily long acquisition times restrict their applications. In this work, we explore the broad utility and underutilized power of a chemical shift-selective one-dimensional (1D) version of a 2D 13 C– 13 C spin-diffusion solid-state NMR technique. The method, which is called 1D dipolar-assisted rotational resonance (DARR) difference, is applied to a variety of biomaterials including lignocellulosic plant cell walls, microcrystalline peptide fMLF, and black widow dragline spider silk. 1D 13 C– 13 C spin-diffusion methods described here apply in select cases in which the 1D 13 C solid-state NMR spectrum displays chemical shift-resolved moieties. Furthermore, this is analogous to the selective 1D nuclear Overhauser effect spectroscopy (NOESY) experiment utilized in liquid-state NMR as a faster (1D instead of 2D) and often less ambiguous (direct sampling of the time domain data, coupled with increased signal averaging) alternative to 2D NOESY. Selective 1D 13 C– 13 C spin-diffusion methods are more time-efficient than their 2D counterparts such as proton-driven spin diffusion (PDSD) and dipolar-assisted rotational resonance. The additional time gained enables measurements of 13 C– 13 C spin-diffusion buildup curves and extraction of spin-diffusion time constants TSD, yielding detailed structural information. Specifically, selective 1D DARR difference buildup curves applied to 13 C-enriched hybrid poplar woody stems confirm strong spatial interaction between lignin and acetylated xylan polymers within poplar plant secondary cell walls, and an interpolymer distance of ~0.45–0.5 nm was estimated. Additionally, Tyr/Gly long-range correlations were observed on isotopically enriched black widow spider dragline silks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Genetic switching by the Lac repressor is based on two-state Monod–Wyman–Changeux allostery

High-resolution NMR spectroscopy enabled us to characterize allosteric transitions between various functional states of the dimeric Escherichia coli Lac repressor. In the absence of ligands, the dimer exists in a dynamic equilibrium between DNA-bound and inducer-bound conformations. Binding of either effector shifts this equilibrium toward either bound state. Analysis of the ternary complex between repressor, operator DNA, and inducer shows how adding the inducer results in allosteric changes that disrupt the interdomain contacts between the inducer binding and DNA binding domains and how this in turn leads to destabilization of the hinge helices and release of the Lac repressor from the operator. Based on our data, the allosteric mechanism of the induction process is in full agreement with the well-known Monod–Wyman–Changeux model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.

Science & Technology - Other Topics↗
DOE OSTI2023

Spatio-temporal and weather characterization of road loads of electrified heavy-duty commercial vehicles across U.S. interstate roads

Adoption of battery electric vehicles (BEV) in heavy duty (HD) commercial freight transportation is difficult due to technological and economic hurdles. Beyond safety and compliance, fleet and operational logistics necessitate both high uptime and parity with diesel system productivity/Total Cost of Ownership to support widespread deployment of electric powertrains. However, relatively high energy storage costs, along with the higher weight of BEV systems, limit the viability of HD commercial freight transport to shorter-range applications where smaller batteries will serve for mission energy requirements (single operational shift). Knowing the energy consumption and operating variations of these commercial vehicle systems is crucial for effectively sizing the energy storage systems. This paper is the first in a series of studies to understand the regional specific operating design domain variations of commercial Class 8 HD trucks and the associated impact to their energy requirements. In particular, the local weather conditions are shown to influence the total vehicle energy usage. Further, the impact of temperature, pressure, and humidity changes are shown to impact the local air density. This is needed to calculate aerodynamic drag on vehicles and has been found to play a significant role in the overall performance of a vehicle. Although the methodology of calculating air density varies only slightly throughout the literature, the application to transportation has still been limited. This study provides a means by which air density can be estimated for the contiguous U.S. using the NOAA MADIS dataset. These air density estimates were then used to determine vehicle performance in varying regions of the country to highlight the importance of consideration of weather variables when monitoring vehicle performance, but also to provide recommendations based on these locales upon fleet conversion to BEVs.

Moore, Amy↗
DOE OSTI2022

Water Network Shape-Dependence of Local Interactions with the Microhydrated –NO 2 – and –CO 2 – Anionic Head Groups by Cold Ion Vibrational Spectroscopy

We report the structural evolutions of water networks and solvatochromic response of the CH 3 NO 2 – radical anion in the OH and CH stretching regions by analysis of the vibrational spectra displayed by cryogenically cooled CH 3 NO 2 – ·(H 2 O) n=1–6 clusters. The OH stretching bands evolve with a surprisingly large discontinuity at n = 6, which features the emergence of an intense, strongly red-shifted band along with a weaker feature that appears in the region assigned to a free OH fundamental. Very similar behavior is displayed by the perdeuterated carboxylate clusters, RCO 2 – ·(H 2 O) n=5–7 (R = CD 3 CD 2 ), indicating that this behavior is a general feature in the microhydration of the triatomic anionic domain and not associated with CH oscillators. Electronic structure calculations trace this behavior to the formation of a “book” isomer of the water hexamer that adopts a configuration in which one of the water molecules resides in an acceptor–acceptor–donor (AAD) (A = acceptor, D = donor) H-bonding site. Excitation of the bound OH in the AAD site explores the local network topology best suited to stabilize an incipient –XO 2 H–OH–(H 2 O) 2 intracluster proton-transfer reaction. These systems thus provide particularly clear examples where the network shape controls the potential energy landscape that governs water network-mediated, intracluster proton transfer. The CH stretching bands of the CH 3 NO 2 – ·(H 2 O) n=1–6 clusters also exhibit strong solvatochromic shifts, but in this case, they smoothly blue-shift with increasing hydration with no discontinuity at n = 6. Furthermore, this behavior is analyzed in the context of the solute-ion polarizability response and partial charge transfer to the water networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Light induced ion migration studies in perovskite solar cell using nonlinear impedance spectroscopy

Complex interactions between mobile ions and charge carriers in perovskite solar cells (PSCs) make it challenging to fully understand their dynamic interplay. Exposure to light further complicates these interactions, altering the system’s dynamics and inducing nonlinear effects that lead to changes in the J−V curve. Understanding these effects is crucial for improving the operational stability of PSCs. Impedance spectroscopy (IS) is a powerful technique for evaluating relaxation processes in the frequency domain; however, it is limited in capturing nonlinear contributions. Here, in this work, nonlinear impedance spectroscopy (NLIS) is employed to analyze the higher harmonic response to AC perturbation, both in the dark and after short-term light exposure. A shift in the low-frequency (LF) higher harmonic peak is observed after open-circuit light exposure, attributed to an altered electric field suggesting ion re-distribution, whereas closed-circuit exposure shows no LF shift, indicating minimal ion movement. Additionally, light exposure reduces higher-order admittance, more notably in open-circuit conditions, suggesting decreased recombination. Temperature-dependent analysis was conducted to characterize the activation energy of migrating species, identifying iodide as the dominant migrating ion.

14 SOLAR ENERGY↗
DOE OSTI2025

Post-hoc reweighting of hadron production in the Lund string model

We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact variation of hadronization parameters on pre-generated event samples. The procedure is post hoc, requiring only a small amount of additional information stored per event, and allowing for efficient estimation of hadronization uncertainties without repeated simulation. Weight expressions are derived from the hadronization algorithm itself, and validated against direct simulation for a wide range of observables and parameter shifts. The hadronization algorithm can be viewed as a hierarchical Markov process with stochastic rejections, a structure common to many complex simulations outside of high-energy physics. This perspective makes the method modular, extensible, and potentially transferable to other domains. We demonstrate the approach in Pythia, including both coverage considerations and timing benefits. For the purpose of this paper, our goal is to develop and demonstrate the the formalism, and we therefore exclude several model variations for baryon production (popcorn model, junction production) needed for proton collisions. These will be the topic of a future paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Sensitivity of back‐projection algorithm (BPA) synthetic aperture radar (SAR) image formation to initial position, velocity, and attitude navigation errors

Abstract The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.

Lindstrom, Colton↗
DOE OSTI2021

Semicomputational calculation of Bragg shift in stratified materials

The fiber Bragg grating (FBG) may be viewed as a one dimensional photonic band-gap crystal by virtue of the periodic spatial perturbation imposed on the fiber core dielectric material. Similar to media supporting Bloch waves, the engraved weak index modulation, presenting a periodic “potential” to an incoming guided mode photon of the fiber, yields useful spectral properties that have been the basis for sensing applications and emerging quantum squeezing and solitons. The response of an FBG sensor to arbitrary external stimuli represents a multiphysics problem without a known analytical solution despite the growing use of FBGs in classical and quantum sensing and metrology. Here, we study this problem by first presenting a solid mechanics model for the thermal and elastic states of a stratified material. Additionally, the model considers an embedded optical material domain that represents the Bragg grating, here in the form of an FBG. Using the output of this model, we then compute the optical modes and their temperature- and stress-induced behavior. The developed model is applicable to media of arbitrary shape and composition, including soft matter and materials with nonlinear elasticity and geometric nonlinearity. Finally, we employ the computed surface stress and temperature distributions along the grating to analytically calculate the Bragg shift, which is found to be in reasonable agreement with our experimental measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A reduced-order shifted boundary method for parametrized incompressible Navier–Stokes equations

Here, we investigate a projection-based reduced order model of the steady incompressible Navier–Stokes equations for moderate Reynolds numbers. In particular, we construct an “embedded” reduced basis space, by applying proper orthogonal decomposition to the Shifted Boundary Method, a high-fidelity embedded method recently developed. We focus on the geometrical parametrization through level-set geometries, using a fixed Cartesian background geometry and the associated mesh. This approach avoids both remeshing and the development of a reference domain formulation, as typically done in fitted mesh finite element formulations. Two-dimensional computational examples for one and three parameter dimensions are presented to validate the convergence and the efficacy of the proposed approach.

"embedded" basis↗
DOE OSTI2025

The congenital APOA1 K107del mutation disrupts the lipid-free conformation of monomeric APOA1 and impairs oligomerization

Apolipoprotein A-I (APOA1) oligomerization is thought to be essential for high-density lipoprotein (HDL) formation and metabolism. Naturally occurring mutations can disrupt normal APOA1 folding and self-association, leading to dysfunctional HDL formation and cardiovascular disease. The congenital APOA1 variant p.K131del (APOA1 K107del ) has been associated with cardiovascular pathologies such as low HDL-cholesterol levels and aortic amyloidosis, and multiple studies indicate structural changes in APOA1 conformation underlie associated dysfunction. In the current study, we confirmed that APOA1 K107del exhibits no notable defect in lipid-binding. However, using polyacrylamide gel electrophoresis (PAGE) and size-exclusion chromatography (SEC), we found that loss of lysine 107 resulted in a remarkable shift in the distribution of APOA1 oligomers with a much higher proportion of monomers present in APOA1 K107del compared to wild-type APOA1. Further investigation using quantitative cross-linking revealed a major disruption of interactions in helical regions reported to participate in domain swaps necessary for proper self-association. This structural disruption appears to impair N- and C-termini interactions and dynamics that lead to non-specific aggregation. In conclusion, these findings support the hypothesis that lysine 107 is critical for proper folding and self-association of lipid-free APOA1 which could impact HDL biogenesis.

APOA1 (p.K131del)↗
DOE OSTI2026

Comparative Evaluation of Spectral Methods for Robust Reactor Noise Estimation

Reactor noise analysis provides a noninvasive means to determine neutron kinetic parameters from stochastic fluctuations in detector signals. However, standard cross-power spectral density (CPSD) analyses can be sensitive to numerical processing choices, which may introduce processing-dependent systematic shifts in estimates of the prompt neutron decay constant (α) and limit reproducibility. This study uses a hybrid multitaper–Welch spectral estimator to analyze subcritical noise measurements from a fast-spectrum critical assembly. The decay constant α was extracted using three frequency-domain methods: the CPSD, the magnitude-squared coherence (MSC), and the generalized magnitude-squared coherence (GMSC). These coherence-based estimators normalize detector auto-spectral structure and are expected to reduce the sensitivity of fitted α values to processing parameters. A Sobol global sensitivity analysis identified which numerical inputs most strongly influence the fitted values of α. All estimators produced a linear dependence of α on inverse count rate, with delayed-critical extrapolations near 1.7 × 10 4 s −1 , in agreement within 8% of MCNP6.3 KOPTS benchmark calculations. Sensitivity results show that while the CPSD depends on both time-bin width and taper selection, the MSC and GMSC are dominated by time-bin width alone, indicating reduced parameter coupling and greater robustness to processing variability. These findings demonstrate the feasibility and practical value of coherence-based spectral estimators for extracting α from reactor noise and support their broader application to multi-detector and irregular datasets in subcritical system characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗