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DOE OSTI2025

Hexagonal Boron Nitride: Physical Properties, Hydride Vapor‐Phase Epitaxy Growth of Large‐Diameter Quasi‐Bulk Wafers and Applications

Hexagonal boron nitride ( h ‐BN), with its ultrawide bandgap and 2D structure, holds an immense promise for advanced semiconductor applications. Scaling bulk crystals to large‐diameter wafers, crucial for complex device fabrication, remains a challenge with high temperature, high pressure, and metal flux solution methods. To address this, recent efforts have focused on hydride vapor‐phase epitaxy (HVPE) for producing large diameter thick h ‐BN quasi‐bulk wafers, reaching hundreds of micrometers. These HVPE‐grown quasi‐bulk crystals exhibit excellent c ‐axis long‐range order. Notably, the in‐plane mobility‐lifetime products for both electrons and holes surpass 10 −4 cm 2 V −1 , which are two orders of magnitude greater than the out‐of‐plane (vertical) values, highlighting the potential for high‐performance devices leveraging superior lateral transport. Lateral detectors fabricated from 100 μm thick B‐10 enriched h‐ BN wafers have achieved a record 60% thermal neutron detection efficiency. Based on its physical properties, h‐ BN appears to be an outstanding material of choice for light‐triggered electronic power switches capable of supporting high ‐ voltage and high ‐ power operations. These recent advancements in large‐diameter h ‐BN quasi‐bulk crystal growth, enabled by HVPE, pave the way for applications spanning deep UV photonics, high‐power electronics, high‐efficiency neutron detection, and quantum information technologies, establishing h ‐BN as both a versatile active semiconductor and an ideal substrate.

Jiang, Hongxing [Department of Electrical and Comp↗

DOE OSTI2025

Designing for cooperative grain boundary segregation in multicomponent alloys

Tailoring the nanoscale distribution of chemical species at grain boundaries is a powerful method to dramatically influence the properties of polycrystalline materials. However, classical approaches to the problem have tacitly assumed that only competition is possible between solute species. In this paper, we show that solute elements can cooperate in the way they segregate to grain boundaries: In properly targeted alloys, the different chemical species cooperate to each fill complementary grain boundary sites disfavored by the other. By developing a theoretical “spectral” approach to this problem based on quantum-accurate grain boundary site distributions, we show how grain boundaries can be cooperatively alloyed, whether by depletion or enrichment. We provide machine-learned cosegregation information for over 700 ternary aluminum-based alloys and experimentally validate the concept in one ternary alloy where cosegregation is not expected by prior models but is expected based on the cooperative model.

grain boundaries↗

DOE OSTI2025

White Paper: Research & Development for the Time at Temperature Approach

Recent advancements in nuclear power research are greatly improving reactor safety and performance through the development of Accident Tolerant Fuel (ATF) and Low-Enriched Uranium Plus (LEU+). These innovations can address Departure from Nucleate Boiling (DNB) margins, which are vital for reactor safety. DNB happens when the coolant switches to film boiling, significantly decreasing heat transfer and posing a risk of fuel cladding failure. The U.S. Nuclear Regulatory Commission (NRC) employs conservative DNB criteria, which can potentially restrict the operational flexibility and efficiency of reactors. The Time at Temperature (TaT) approach could provide a more detailed and adaptable operational guideline by establishing acceptable time-temperature limits, accounting for the duration a material can withstand elevated temperatures without losing its integrity. This method allows reactors to operate more efficiently and safely, offering additional operational margins, faster power adjustments, and improved fuel cycle economics. TaT criteria allow for higher power levels and more flexible responses to operational transients, particularly applicable for anticipated operational occurrences (AOOs) that result in short durations of post-DNB conditions. It enhances plant operational flexibility, allows faster startup times, and enables quicker power level adjustments, optimizing fuel loading patterns and improving fuel cycle economics. Implementing TaT limits reduces core design constraints, lowers fuel usage, and reduces costs, essential for the long-term sustainability of Light Water Reactors (LWRs). TaT maximizes the use of advanced fuel technologies like ATF and LEU+, further enhancing their economic and environmental benefits. To apply the TaT approach in existing LWRs, collaborative research activities among various DOE-sponsored programs are essential. These efforts should incorporate fuel experiments, physics-based high-fidelity modeling, ML-based surrogate modeling, and optimization techniques. This whitepaper proposes four research and development areas: 1) Investigation of the feasibility of new operations of LWR with updated safety limits; 2) Assessment of reactor operation limits through uncertainty reduction; 3) Evaluation of power uprate in virtual environment; and 4) Lattice and reactor core design for power uprate. Each area includes why this research is in need and a suggested scope of work. These comprehensive research areas ensure practical and beneficial advancements for existing reactors, translating innovations in nuclear fuel and cladding technology into improved reactor performance and safety.

42 - ENGINEERING↗

DOE OSTI2025

Wood template-supported phase change material composites for durable and form-stable thermal energy storage in buildings

Here, to reduce and shift peak energy loads in buildings, phase change materials (PCMs) with high transition enthalpies and transition temperatures near human thermal comfort are desirable for thermal energy storage (TES). Traditional solid/liquid PCMs suffer from leakage during thermal cycling, requiring encapsulation that lowers heat storage capacity. Wood templates (WTs), with porous and hierarchical structures, provide natural encapsulation scaffolds for PCM containment. Polyethylene glycol (PEG) is a compatible PCM with WTs, but when infiltrated alone, ~30 wt% of PEG binds to wood with no detectable phase change, limiting TES efficiency. To address this, we developed a method to (1) improve the form stability of balsa and pine-based composites (BWT + PCMs and PWT + PCMs) and (2) reduce inactive PCM within the composites to ~10 wt%. BWT + PCMs exhibit transition properties upwards of 114.2 J/g at 25.4 degrees C, with no degradation after 1000 thermal cycles, and similar stiffness compared to raw balsa. Meanwhile, PWT + PCMs exhibit 25 % higher storage efficiency with the addition of poly(ethylene glycol) diacrylate compared to solely PEG-infiltrated PWT. PWT + PCM retains 84 % of raw pine's mechanical stiffness, sufficient for light-duty construction. Our shape-stabilized WT + PCM composites enrich the functionality of wood materials as both ideal TES material candidates and light-duty building construction applications.

25 ENERGY STORAGE↗

DOE OSTI2025

Technology Case Study: Techno-Economic and Life Cycle Analysis for Microalgae Conversion Pathways to Fuels and Products

This technology case study report details the cost and sustainability prospects for an emerging feedstock - microalgae - converted to fuels and products via a fractionation and upgrading approach termed combined algae processing (CAP). Detailed techno-economic analysis (TEA) and life cycle analysis (LCA) are conducted for the conversion of farmed algae biomass, with two primary scenarios considering the conversion of either high-compositional-quality biomass enriched in lipids (high-lipid [HL]) or lower-quality biomass enriched in protein (high-protein [HP]). Each scenario employs a different biorefinery configuration tailored towards extracting the maximum value from the given biomass composition. The HL scenario produces fuels and non-isocyanate polyurethane (NIPU) as the primary products, while the HP scenario products fuels and a residual solid coproduct which can be used as a co-feed for producing thermoplastics. The results for the HL scenario were particularly promising, with a minimum fuel selling price (MFSP) of $\$$3.68 per gasoline gallon equivalent (GGE) and fuel GHG emissions translating to 54%-76% reduction compared to petroleum fuels depending on the coproduct handling method used. In contrast, the HP scenario faced more challenges in producing biofuels economically, projecting an MFSP of $\$$7.92/GGE despite significant revenues from the residual algae solids. LCA results for the HP case reflected a 24% reduction potential in biorefinery-level GHG emissions. However, these GHG reductions were primarily associated with the thermoplastic coproduct, which accounted for 93% of all biorefinery outputs by mass. Using a process-level allocation method, carbon intensity results were less promising, indicating a net increase in fuel GHG emissions versus petroleum fuels and highlighting the reliance of this scenario on the thermoplastic coproduct.

09 BIOMASS FUELS↗

DOE OSTI2024

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

UV-Vis spectrophotometric determination of rare earth elements (REE) speciation at near-neutral to alkaline pH. Part II: hydrolysis of Er from 25 to 75 °C

Aqueous speciation of rare earth elements (REE) controls their mobilization, fractionation, and enrichment in the natural waters. Geochemical modeling of their speciation is key to improve our understanding of the formation of economic mineral deposits, for developing mineral separation and mine tailing recovery technologies, and for characterizing the geochemistry of thermal water. However, our ability to predict the fate of REE in a wide pH and temperature range is limited by the scarcity of thermodynamic data for the REE hydroxyl complexes. In part I of this study (H. J. Han and A. P. Gysi, Dalton Trans., 2024, 53, 13129–13141), the optical properties of m-cresol purple (mCP) were determined using UV-Vis spectrophotometry between 25 and 75 °C in order to develop a method for deriving the hydrolysis constants of erbium (Er). Here, UV-Vis spectrophotometry experiments were conducted as a function of temperature between 35 and 75 °C to determine the hydrolysis of Er in near-neutral to alkaline solutions using mCP as an in situ pH color indicator. Here, the experiments were conducted with Er concentrations from 0 to ~0.253 mmol kg –1 in low ionic strength solutions (≤0.001 mol kg –1 ).

58 GEOSCIENCES↗

DOE OSTI2025

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗

DOE OSTI2024

RNA Splicing Events in Circulation Distinguish Individuals With and Without New-onset Type 1 Diabetes

Context: Alterations in RNA splicing may influence protein isoform diversity that contributes to or reflects the pathophysiology of certain diseases. Whereas specific RNA splicing events in pancreatic islets have been investigated in models of inflammation in vitro, how RNA splicing in the circulation correlates with or is reflective of type 1 diabetes (T1D) disease pathophysiology in humans remains unexplored. Objective: To use machine learning to investigate if alternative RNA splicing events differ between individuals with and without new-onset T1D and to determine if these splicing events provide insight into T1D pathophysiology. Methods: RNA deep sequencing was performed on whole blood samples from 2 independent cohorts: a training cohort consisting of 12 individuals with new-onset T1D and 12 age- and sex-matched nondiabetic controls and a validation cohort of the same size and demographics. Machine learning analysis was used to identify specific isoforms that could distinguish individuals with T1D from controls. Results: Distinct patterns of RNA splicing differentiated participants with T1D from unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects. Conclusion: Alternative RNA splicing events in whole blood are significantly enriched in individuals with new-onset T1D and can effectively distinguish these individuals from unaffected controls. Further, our findings also suggest that RNA splicing profiles offer the potential to provide insights into disease pathogenesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Joint Sample Analyses of Nuclear Forensic Materials Provided by the Republic of Kazakhstan: U. S. Laboratory Results

This document serves as an interim report to summarize sample analyses performed by Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL) on a series of five nuclear forensics samples provided by the Institute of Nuclear Physics (INP) in the Republic of Kazakhstan. The sample set contains four uranium oxide powders and one low-enriched uranium fuel pellet. The samples were provided as part of a broader collaboration that involved a set of joint sample analyses conducted by INP and the US National Laboratories. The joint analyses are being conducted using well-developed analytical plans. These activities are designed to support the advancement of nuclear forensic science and capacity building in all three institutes in both countries. This report builds upon an earlier preliminary summary of the joint interactions and will be augmented by a final report. The final report will summarize the results and value of all sample analyses performed at the three institutes (INP, LLNL, and LANL). The final report will also provide an intercomparison of the results, analytical methods and best practices employed, as well as outline future collaborative nuclear forensics activities that are being developed in the Kazakhstan region.

and nuclear chemistry↗

DOE OSTI2026

Impact of Time-Dependent Reactor and Sensor Physics on Core Power Synthesis (Rev.1)

Online synthesis of power distribution is critical in the operation and control of nuclear power reactors to ensure that the core is operating within safety margins and to provide essential knowledge associated with the burnup of the fuel. In light-water reactors, power synthesis is achieved by using some a priori knowledge of the state of the reactor core and updating based on the signals coming from in-core sensors—namely, self-powered neutron detectors (SPNDs). This report examines the effects of fuel burnup and sensor degradation on the ability to accurately synthesize the power distribution in a pressurized water reactor (PWR), considering the typical low-enriched uranium (LEU, 3%-5% enrichment) fuel cycle as well as the higher enrichment LEU+ (5%-8% enrichment) fuel cycle. Several modeling tools were used to simulate power synthesis based on the responses of SPNDs, with emitters made out of Rh or V. A representative PWR LEU core was modeled using the Polaris/Purdue Advanced Reactor Core Simulator (PARCS) approach. The Monte Carlo N-Particle Transport 6 (MCNP6) code was used as well to calculate response functions between different segments of fuel to individual SPNDs; this is a crucial parameter for power synthesis. The Oak Ridge Isotope GENeration (ORIGEN) package in the Standardized Computer Analyses for Licensing Evaluation (SCALE) code was used to model the time-dependent isotopic transmutation in the SPND emitters. All these data were fed into a custom code that enacted the point-based iterative method to simulate power synthesis. Developmental work was also performed on high-fidelity SPND models in the GEometry ANd Tracking 4 (Geant4) code, which enables higher-accuracy modeling of the current responses from SPNDs. In this work, five sets of time-dependent power synthesis test cases were conducted. In these test cases, systematic changes in the input conditions enabled an analysis of the effect of (1) slightly inaccurate a priori power distribution assumptions with respect to fuel burnup, (2) highly inaccurate a priori power distribution assumptions with respect to fuel burnup (such that burnup is not included in the a priori assumed distribution), and (3) differences between Rh and V SPNDs in terms of downstream consequences of the transmutation in the emitters and the extended nature of the LEU+ fuel cycle in comparison with LEU. The authors discovered that one may permissibly have slightly inaccurate a priori assumptions of the fuel burnup (such that the level of burnup may be slightly underapproximated or overapproximated by the accumulated burnup in approximately 9.3 full power days), but to not account for burnup at all in the a priori assumptions leads to severe levels of error, approaching 25% at maximum (for LEU). The authors also discovered that V SPNDs are extraordinarily robust in both the LEU and LEU+ fuel cycles considered in this modeling work, whereas Rh SPNDs undergo significant transmutation that can result in large errors in the synthesized power distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Dehydrogenation vs Apparent Hydrogenation: Unraveling the Mechanisms of He and O 2 Plasma Etching on Colloidal Nanocrystal Films

Removing organic ligands from colloidal nanoparticles is critical for fabricating solid-state devices, yet accurately quantifying this removal remains a significant analytical challenge. Here, we establish a robust and accessible method for this quantification by calibrating Raman spectroscopy against precise ion beam analysis (IBA) for nanoparticle assemblies (CNAs) processed by helium (He) and oxygen (O 2 ) plasmas. We demonstrate that the calibration curves are remarkably independent of plasma power and pressure, depending critically only on the choice of feed gas. He plasma induces rapid dehydrogenation and cross-linking, evidenced by a much faster decrease in the C–H Raman signal relative to the actual carbon loss. Conversely, O 2 plasma leads to a surprising “apparent hydrogenation”, where the carbon backbone is removed significantly faster than the C–H signal diminishes. This counterintuitive effect is explained by a serial mechanism of oxidative fragmentation; β-scission cleaves the alkyl chains, and subsequent stabilization steps enrich the remaining film with hydrogen-rich methyl-terminated fragments, while carbon is efficiently removed as volatile CO. This work provides calibrated functions that enable the rapid determination of absolute carbon content in processed CNAs using simple Raman spectroscopy with uncertainties of ∼8% for O 2 and ∼12% for He plasma, offering a vital tool for both process diagnostics and fundamental studies of plasma–matter interactions in colloidal nanocrystal films.

Animal feed↗

DOE OSTI2025

UnigeneFinder: An Automated Pipeline for Gene Calling From Transcriptome Assemblies Without a Reference Genome

ABSTRACT For most species, transcriptome data are much more readily available than genome data. Without a reference genome, gene calling is cumbersome and inaccurate because of the high degree of redundancy in de novo transcriptome assemblies. To simplify and increase the accuracy of de novo transcriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared with existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high‐quality reference genomes. These features, coupled with the pipeline’s cross‐platform implementation, focus on automation, and an accessible, user‐friendly interface, make UnigeneFinder a useful tool for many downstream sequence‐based analyses in nonmodel organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high‐performance computing (HPC) systems and personal computers, further reducing barriers to use.

Xue, Bo [Plant Resilience Institute Michigan State↗

DOE OSTI2025

Application of PRIM for understanding patterns in carbon dioxide model-observation differences

Reducing uncertainties in regional carbon balances requires a better understanding of CO 2 transport in synoptic weather systems. Here, we apply the Patient Rule Induction Method (PRIM), a data-mining method to identify high-density regions for a target-class within an input parameter space, to airborne observations of potential temperature, wind speed, water vapor mixing ratio, and CO 2 dry mol fraction gathered during the Atmospheric Carbon and Transport (ACT)-America Summer 2016 and Winter 2017 campaigns. ACT observations were targeted at expert-designated cases of fair weather and near-frontal warm and cold sector air at atmospheric boundary-layer, lower-, and higher free tropospheric levels (ABL, LFT, and HFT, respectively). We investigate atmospheric characteristics of these pre-defined cases and associated CO 2 model-observation-differences in the mesoscale WRF-Chem model. PRIM results separate winter- and summertime observations as well as observations from ABL, LFT, and HFT with enrichment factors of 4.0–20.5 inside the PRIM box compared to the entire dataset but cannot distinguish between near-frontal warm and cold sector observations in the higher free troposphere. Analyzing of the parameter space constrained by PRIM, we find that large magnitude model observation differences preferentially associated with times when atmospheric conditions are less typical. This association suggests that PRIM could provide a useful tool for isolating atmospheric conditions with large-magnitude and non-Gaussian CO 2 -residuals for targeted transport model evaluation and to potentially improve inversion results during synoptically active periods.

Gerken, Tobias [James Madison Univ., Harrisonburg,↗

DOE OSTI2024

Status of h-BN quasi-bulk crystals and high efficiency neutron detectors

III-nitrides have fomented a revolution in the lighting industry and are poised to make a huge impact in the field of power electronics. In the III-nitride family, the crystal growth and use of hexagonal BN (h-BN) as an ultrawide bandgap (UWBG) semiconductor are much less developed. Bulk crystals of h-BN produced by the high-temperature/high-pressure and the metal flux solution methods possess very high crystalline and optical qualities but are impractical to serve as substrates or for device implementation as their sizes are typically in millimeters. The development of crystal growth technologies for producing thick epitaxial films (or quasi-bulk or semi-bulk crystals) in large wafer sizes with high crystalline quality is a prerequisite for utilizing h-BN as an UWBG electronic material. Compared to traditional III-nitrides, BN has another unique application as solid-state neutron detectors, which however, also require the development of quasi-bulk crystals to provide high detection efficiencies because the theoretical efficiency (ηi) relates to the detector thickness (d) by ηi=1−e−dλ, where λ denotes the thermal neutron absorption length which is 47 μm (237 μm) for 10B-enriched (natural) h-BN. We provide an overview and recent progress toward the development of h-BN quasi-bulk crystals via hydride vapor phase epitaxy (HVPE) growth and the attainment of thermal neutron detectors based on 100 μm thick 10B-enriched h-BN with a record efficiency of 60%. The thermal neutron detection efficiency was shown to enhance at elevated temperatures. Benchmarking the crystalline and optical qualities of h-BN quasi-bulk crystals with the state-of-the-art mm-sized bulk crystal flakes and 0.5 μm thick epitaxial films identified that reducing the density of native defects such as vacancies remains the most critical task for h-BN quasi-bulk crystal growth by HVPE.

Physics↗

DOE OSTI2024

Inferring the Focal Depths of Small Earthquakes in Southern California Using Physics-Based Waveform Features

Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ~8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2 < M L < 4) recorded at local distances (<150 km), for which depth estimation is especially challenging. We find that differential magnitudes (M w /M L –M c ) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡log 10 (M 0 )+3log 10 (f c ), and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. Here, we also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.

58 GEOSCIENCES↗

DOE OSTI2025

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan details the strategy, mission, scope, and goals—both near-term and long-term—along with the structure and organization of nuclear fuels and materials research, development, and demonstration (RD&D) activities within the Fuel Cycle Technologies (FCT) program. The FCT program, tasked by the U.S. Department of Energy (DOE), employs a science-based approach to advance fuel technologies. This approach integrates theory, experiments, and multi-scale modeling and simulation (M&S) to develop a predictive understanding of fuel fabrication processes and fuel/cladding performance under irradiation, moving beyond traditional empirical methods. The long-term goals of the AFC are guided by the AFC Strategic Plan and align with the DOE Office of Nuclear Energy (NE) Roadmap [1], which outlines a multi-decade vision for demonstrating and qualifying advanced fuel forms to support diverse fuel cycle options. Near-term goals focus on enhancing accident tolerant fuels (ATF) for Light Water Reactors (LWR), a significant challenge that demands balancing immediate objectives with ongoing progress toward advanced reactor missions. Accelerating the traditional fuel qualification process to meet ATF objectives is another critical challenge. A detailed set of 5-year goals, summarized below, has been developed in line with the overarching science-based fuel development approach: • Advanced LWR Fuel Technologies: By 2027, support the development of advanced LWR fuel technologies with improved performance and enhanced accident tolerance. This includes high burnup (HBu), low enriched uranium (LEU)+, coated cladding, and doped fuel, aimed at complementing industry-led significant LWR uprates and plant refurbishments. • Tristructural Isotropic (TRISO) Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Metal Fuel: Achieve qualification by 2028 and develop improved designs for emerging markets. • Molten Salt Fuel: By 2027, deploy a robust program that enables fuel salt qualification technologies needed to support fuel salt research and development (R&D), focusing on emergent needs to derisk fuel salt production and utilization in advanced reactors. • Long-Term ATF: Develop fuel technologies that enable significant power uprates (~50%) in refurbished or new LWRs while optimizing fissile material utilization and waste disposal. The 5-year milestones in the AFC Execution Plan are contingent on an assumed budget. This Execution Plan will be updated annually to reflect actual funding profiles as budget guidance becomes available, ensuring milestones are adjusted accordingly. In summary, the AFC Execution Plan presents a comprehensive strategy to advance nuclear fuel technologies through a science-based approach, addressing both near-term and long-term goals while adapting to funding realities.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

The POINTER Imaging baseline cohort: Associations between multimodal neuroimaging biomarkers, cardiovascular health, and cognition

Abstract INTRODUCTION The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study. METHODS Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta‐amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk. RESULTS A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ−, Aβ+ (29%) participants were older, had higher apolipoprotein E (APOE) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition. DISCUSSION In this heterogenous, at‐risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non‐AD (Alzheimer's disease) pathway to brain injury and cognitive decline. Highlights The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle POINTER Imaging collected multimodal neuroimaging data in this unique, at‐risk cohort Amyloid burden was related to age, apolipoprotein E (APOE) ε4 carriage, and measures of disease progression Associations between amyloid and tau, and tau and cognition, were relatively weak CVD risk and tau pathology were independently related to memory

Neurosciences & Neurology↗