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DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗

DOE OSTI2023

Multi-Source Data Aggregation and Real-Time Anomaly Classification and Localization in Power Distribution Systems

This paper proposes a real-time anomaly location and classification framework for power distribution systems to simultaneously determine the type of anomaly (i.e., short-circuit fault, cyber attack, DER switching) and its location. The proposed framework employs the data aggregation module to collect the measurement data from multiple field devices operating at different sampling rates, such as protection relays and D-PMUs. The output of the data aggregation is then fed into a multi-task learning-based long-based short-term memory (MTL-LSTM) to classify the type of anomaly and the location in two separate tasks. The proposed MTL-LSTM approach can be utilized in real-time operation in order to distinguish between normal and several anomalous operations and locate the anomaly. The proposed framework is tested on a modified IEEE 33-bus test feeder benchmark that integrates solar generation and energy storage. Furthermore, the results show that the proposed framework can locate and classify anomalies for several operation conditions with more than 96% accuracy. Further experiments highlight the impact of aggregating multiple sources of data on the performance of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Enabling Low-Temperature (LTP) Ignition Technologies for Multi-Mode Engines through the Development of a Validated High-Fidelity LTP Model for Predicative Simulations Tools

The goal of multi-mode engine architectures is to extend current lean-burn dilution limits with renewable fuels, which requires spark plugs to deposit high energies (hundreds of mJ) in order to initiate ignition and complete combustion. At elevated energy deposition rates, spark plugs experience increased electrode erosion and thermal losses, which ultimately shortens the spark-plug lifetime and lowers ignition efficiency. As such, in order to safeguard the efficiency gains of multi-mode concepts, new and improved ignition technologies are required. Recently, non-equilibrium low-temperature plasmas (LTP) have been shown to promote energy-efficient ignition via quenching and transport of electronically excited atoms and molecules, selective radical production and fast heating of hydrocarbon/air mixtures [1-2]. Thus, LTP is seen as a technology that can potentially improve the energy extraction efficiency of fuels, while enabling kinetically controlled combustion modes towards fuel leaner conditions to realize current DOE VTO goals of improving the sustainability of future mobility [3]. Although many previous studies have demonstrated the efficacy of plasma-assisted ignition to enhance combustion, the detailed enhancement mechanisms remain largely unknown, especially for oxygenated fuels and at elevated pressures that are most relevant to practical engine conditions. These barriers hinder the development of accurate and comprehensive numerical models that seek to describe LTP-based ignition in existing engine design software tools and methods. Current state-of-the-art simulation capabilities for LTP ignition systems are in need of improvements since they deliver qualitative results only due to important limitations of existing approaches. Firstly, validated kinetic models with elementary steps for plasma discharges in oxygenated fuel/air mixtures of relevance to the transportation sector are required. Such kinetic models do not exist at present and will be developed and validated within this project. Secondly, plasma discharges and reactive mixture ignition are multi-scale, unsteady processes requiring high-performance numerical methods and software that execute efficiently on DOE supercomputers. Such software does not exist at present and will be developed and applied to practical LTP ignition scenarios as part of this project. Thirdly, experimental databases that are tailored to serve as benchmark in support of the development of predictive computational models of LTP ignition do not exist and will be part of this project.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Cost Analysis of Heavy-Duty Vehicle Proton Exchange Membrane Fuel Cell Stationary Power Plants

Heavy-duty PEM fuel cells could be a low-cost, low emission alternative to combustion turbines for re-electrifying hydrogen if used as part of a long duration grid energy storage systems. Many studies expect heavy duty PEM fuel cell production costs to reduce as manufacturing volumes ramp up and their expected durability of 25,000-30,000 hours aligns well with a 30-year life for hydrogen seasonal energy storage plants that would likely operate less than 10% of the year. The labor, material, and equipment costs associated with installing PEM fuel cells and their required balance of plant for stationary applications have not been thoroughly explored, however. This study performs a detailed design and cost analysis of a 100 MW stationary PEM fuel cell power plant, capturing costs such as cooling, power electronics, pipes, valves, fittings, cabling, conduit, concrete foundations, buildings, and land. It employs methods consistent with NLR's solar PV benchmarking cost analysis and annual technology baseline to derive the total installed costs of stationary PEM fuel cell plants that utilize heavy duty PEM fuel cells.

08 HYDROGEN↗

DOE OSTI2022

Investigation of the impact of difference between FRENDY and NJOY2016 on neutronics calculations

A nuclear data library is used as a starting input for all subsequent neutronics calculations. NJOY has been used worldwide as a nuclear data processing code to create cross section libraries for a long time. For the verification of NJOY method and for providing an alternative nuclear data processing tool, JAEA has been developed the new nuclear library processing code FRENDY. In this paper, nuclear calculations were performed using the ACE files and the multigroup libraries created by both FRENDY and NJOY, and the impacts on the neutronics characteristics due to nuclear data processing were investigated using those libraries. MCNP was used to compare the ACE files by calculating many benchmark problems including ICSBEP and it was confirmed that the k-eff values generally agree with each other within the range of statistical errors. The multigroup cross sections are verified by the BWR design codes LANCR/AETNA through calculation of a commercial BWR5 equilibrium core loaded with 9*9 fuels. It was confirmed that fuel assembly and core characteristics are consistent with each other. From the above investigations, it was confirmed that FRENDY can provide comparable continuous/multi-group neutron cross sections with NJOY. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation

This Final Scientific and Technical Report summarizes work performed under the Phase IIA SBIR project “Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation” (DE-SC0020906) from August 2023 through August 2025. The objective of the Phase IIA effort was to mature and harden capabilities developed during Phase II, with the goal of enabling practical interoperability between Coreform’s isogeometric analysis (IGA) technologies and the Multiphysics Object-Oriented Simulation Environment (MOOSE), while improving robustness, performance, and scalability for complex, nuclear-relevant geometries. Over the course of Phase IIA, the project established and validated an extraction-based interoperability pathway between Coreform tools and MOOSE. A combined mesh and matrix format was defined collaboratively with MOOSE developers and integrated into the solver, enabling standard MOOSE workflows to operate on data exported from Coreform’s IGA and Flex Representation Method (FRM) pipelines. Early demonstrations validated architectural compatibility using linear solid mechanics problems, while later efforts focused on benchmark testing and external use. By the end of the project period, engineers at BWXT were able to independently set up and execute a simulation using the Coreform–MOOSE workflow and provide direct feedback that informed further refinement. In parallel, substantial effort was devoted to improving the robustness of trimmed U-spline construction for complex CAD geometries. A growing test suite of nuclear-relevant models was compiled through collaboration with multiple stakeholders and used to drive extensive bug fixing and reliability improvements. These efforts resulted in improved robustness and performance, including the addition of fallback capabilities that enhance reliability when the underlying commercial CAD kernel fails. Performance-oriented work progressed later in the project, with the development and demonstration of methods to decompose complex geometries into structured subregions and updated data representations to support more efficient solver processing. Additionally, extensive enhancements to threadsafe parallel data structures and trimming operations established a foundation for scalable processing of large assemblies. Collaboration with Sandia National Laboratories on the SGM geometric modeling kernel advanced to a functioning interface test case, positioning the workflow for future kernel integration. Overall, the Phase IIA effort successfully transitioned the project from architectural proof-of-concept to externally exercised, solver-integrated capability, while clarifying remaining technical challenges related to standardization, performance optimization, and kernel integration.

42 ENGINEERING↗

DOE OSTI2022

Preliminary benchmarks and analysis of boundary conditions in a trenched microstructured silicon radiation detector

Microstructured neutron detectors have the benefit of enhanced neutron detection efficiency as compared to planar devices, achieved by etching 6 LiF-filled trenches on the top surface of a silicon PIN diode. This sensor geometry results in a complex electric field distribution and depletion characteristics within the diode under reverse bias. For the first time on record, the effects of a fixed oxide charge on the microstructured device depletion characteristics and mobile carrier transport is investigated. Prototype detectors were fabricated with non-conformal surface doping. Capacitance voltage and current voltage measurements were performed for these prototypes and compared with COMSOL Multiphysics simulations. A spectral response from an 241Am alpha particle source was acquired and analyzed. It was found that monoenergetic alpha particles produce three prominent peaks in the pulse height spectrum output by the device. The peaks were confirmed by simulations to correlate with dead layers and incident trajectories into the microstructure. It was also found that significant differences in pulse rise time result, corresponding with events arriving in a low-field region in the fins and a high-field region in the bulk. Geant4 was utilized for radiation transport, interaction modeling, and benchmarking the spectral data. The results of this simulation work provide confidence in the ability to attain and benchmark electrical characteristics and spectral data for semiconductor radiation detectors employing complex microstructures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

Array↗

DOE OSTI2026

Validation of 266 nm UV Raman thermometry using the alpha-to-beta quartz transition

Ultraviolet Raman scattering is a promising diagnostic for temperature measurements relevant to dynamic compression experiments, where improved signal strength and reduced background are critical. In this work, we experimentally validate Raman thermometry at 266 nm using crystalline quartz heated through the α to β phase transition in a tube furnace. Quartz provides a useful benchmark because of its well-characterized transition temperature near 846 K at ambient pressure. Temperatures inferred from the Stokes to anti-Stokes intensity ratio are compared with thermocouple measurements and with the observed phase transition. Single-shot Raman temperatures track the thermocouple closely, with an empirical single-shot scatter of approximately ±15 K under the present conditions. Temporal averaging reduces shot-to-shot scatter and improves agreement between the Raman temperatures and thermocouple readings to within approximately 8 K, while the transition is identified within 5 K of the accepted value. Measurements are performed using the fourth harmonic of a nanosecond pulsed Nd:YAG laser at 266 nm. The shorter excitation wavelength provides enhanced Raman scattering efficiency and reduced visible background relative to conventional visible excitation. Comparison with 532 nm Raman measurements acquired in the same experimental geometry demonstrates substantially improved signal-to-noise ratio for the UV configuration. These results establish 266 nm Raman thermometry as a reliable and accurate temperature diagnostic under controlled heating conditions and provide a quantitative benchmark for future application in dynamic compression and shock physics experiments.

Chemistry - Chemical explosives↗

DOE OSTI2024

Accelerated Design of Cost-Effective Thermal/Environmental Barrier Coatings based on High-Entropy Rare Earth Disilicates: A First-Principles Study

This project aims to design cost-effective thermal/environmental barrier coatings (TEBC) based on high entropy rare earth disilicates to protect SiC-based ceramic matrix composites from chemical and thermal attack for better performance of components in the hot section of gas turbine engines. To accelerate the alloy design, we utilize first-principles density functional theory (DFT) together with combinatorial chemistry methodology to predict key properties including phase stability, apparent bulk coefficient of thermal expansion (ABCTE), intrinsic lattice thermal conductivity, and temperature-dependent elastic constants. Specifically, this project focuses on β-RE2Si2O7 (RE=Yb, Y, Er, Lu, La, Ce,) with β-Yb2Si2O7 and β-Y2Si2O7 as the benchmark. Our DFT calculations predict that Er1/4Lu1/4Y3/4Yb3/4Si2O7 and Er1/2Lu1/2Y1/2Yb1/2Si2O7 have ultralow lattice thermal conductivity < 0.23 W/m/K at 1500 K and a good match of average ABCTE (5.1 - 5.2×10-6 K-1) with SiC. Owing to the low cost and abundant supply of Ce and La, the A- and G-La2Si2O7/Ce2Si2O7 disilicates are also studied. Our study shows that G-phase Ce2Si2O7 has an ultralow thermal conductivity (0.26 W/m/K at 1500 K) and the apparent bulk ABCTE (≈6.9×10-6 K-1) slightly higher than SiC, demonstrating great potential as low-cost high-performance T/EBC. However, La2Si2O7 and Ce2Si2O7 undergo an A-phase to G-phase polymorphic transition at around 1470 K.

environmental barrier coatings↗

DOE OSTI2021

Material Interactions in Severe Accidents – Benchmarking the MELCOR V2.2 Eutectics Model for a BWR-3 Mark-I Station Blackout: Part II – Uncertainty Analysis

Single case comparisons between severe accident simulations can provide detailed insights into severe accident model behavior, however, they cannot offer insights into model uncertainty, sensitivity to uncertain parameters, or underlying model biases.Here in this analysis, the single case benchmark comparison of the MELCOR material interaction models for a station blackout (SBO) scenario of a boiling water reactor (BWR) using representative Fukushima Daiichi Unit 1 boundary conditions is expanded to include an uncertainty analysis. As part of this uncertainty analysis, 1200 simulations are performed for each material interaction model (2400 total), with random sampling of 14 uncertain MELCOR input parameters. Input parameters are selected for their impact on models representing core degradation processes. These include candling, fuel rod failure, debris quenching and dryout. The analysis performed here is not a traditional “best-estimate” uncertainty analysis that uses best-estimate parameters or identifies best-estimate figure of merit distributions. Instead, it is an exploratory uncertainty analysis that identifies and interrogates underlying model form biases of the two material interaction models (eutectics and interactive materials models). Uniform distributions are applied to all uncertain parameters to ensure coverage of the model parameter uncertainty space. Key findings from this study include underlying model form biases exhibited by material interaction models, and notable differences in accident progression outcomes between the material interaction models. This uncertainty study extends and confirms the conclusions from the first part of this study, which compared the impact of material interaction modeling on simulation of a short-term station blackout scenario with representative Fukushima Daiichi Unit I boundary conditions. In particular, this study confirms that the eutectics model generally exhibits accelerated degradation and failure of fuel components, the core plate, and the lower head. The eutectics model also has a tendency to exhibit a greater degree of core degradation, greater debris mass formation, and larger debris mass ejection. Finally, the eutectics model exhibits higher maximum temperatures for fuel, cladding, particulate debris, oxidic molten pool, and metallic molten pool components than the interactive materials model; interactive materials model simulations exhibit a soft “limitation” on maximum temperatures that is related to the temperature at which material relocation occurs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Impact of recent ENDF nuclear data update, high initial enrichment and high burnup fuel on critical experiments applicability determination via the integral index c k for burnup credit validation

In 2012, NUREG/CR-7109 reported on the validation of burnup credit calculations involving major and minor actinides and major fission products which was investigated for pressurized and boiling water reactor (PWR and BWR) fuel enrichments up to 5 wt% 235 U and assembly-average burnups up to 60 GWd/MTU. Recently, there has been interest in increasing the maximum enrichment used in PWR fuel as high as 8 wt% 235 U and correspondingly increasing the maximum assembly-average burnups to approximately 75 GWd/MTU. These proposed increases in enrichment and burnup necessitate reinvestigation of the validation basis for k eff calculations for this expanded application space. Additionally, the 2012 study was performed by using the Evaluated Nuclear Data File (ENDF)/B-VII.0 nuclear data with the SCALE 6 covariance library, and the effects of using the newly released ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data and covariance libraries should be evaluated. In this work, published in NUREG/CR-7309 in 2025, the validation assessment was performed consistently with NUREG/CR-7109: modeling irradiated fuel assemblies in the Generic Burnup Credit (GBC)-32 cask defined in NUREG/CR-6747. The TSUNAMI-3D sequence was used to generate sensitivity data for the application model, and the data were compared with sensitivity data from select benchmark models. The integral parameter c k is the metric of similarity used in this study and is consistent with NUREG/CR-7109, where a c k value in excess of 0.8 indicates sufficient similarity for use in validation. A new set of benchmark experiments with sensitivity data has been assembled for this effort. The number of experiments with available sensitivity data is now 2,104, compared to 474 in NUREG/CR-7109. This increase was facilitated by the efforts of the Nuclear Energy Agency to generate sensitivity data for a majority of the experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook to supplement the data available in the Oak Ridge National Laboratory (ORNL) Verified, Archived Library of Inputs and Data (VALID). The complete set of benchmarks considered here includes experiments for low-enriched uranium (LEU), intermediate enriched uranium (IEU), and a mixture of uranium and plutonium (MIX) from the ICSBEP Handbook and VALID, as well as ORNL models of the Haut Taux de Combustion (HTC) experiments and other potentially relevant models not included in VALID. The updated similarity study shows that none of the extended burnup and higher enrichment combinations considered show a significant decrease in the number of potentially applicable experiments, meaning sufficient critical experiments exist for the validation of BUC criticality safety calculations, with initial enrichments up to 8 wt% 235 U and burnups up to 80 GWd/MTU. Additionally, both the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries can be used for validation since the number of critical experiments applicable for validation increases for most cases with the most recent nuclear data compared to the previous one. As in previous BUC validation studies, the French HTC experiments are the most similar in a majority of the application cases studied, especially from representative discharge burnups ranging from 40 to 80 GWd/MTU. In conclusion, these results match the conclusions presented in NUREG/CR-7109 regarding validation of the primary actinides in BUC analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Development of a low-pressure Multi-Mesh THGEM detector for fission experiments at FRIB

Abstract A small-area imaging detector prototype for position measurement of fission fragments produced in low energy heavy-ion reactions is presented in this study. The detector readout is equipped with a 2-dimensional, position-sensitive gaseous avalanche readout based on the novel Multi-Mesh Thick-GEM (MM-THGEM), and is coupled to a delay-line board for particle localization. The prototype has an effective area of 10 × 10 cm 2 and is operated in isobutane at low pressure (7–10 Torr). We present and discuss a series of systematic evaluation tests performed by irradiating the detector with α-particles and fission fragments emitted by small-rate sources ( 241 Am, 249 Cf and 252 Cf). Position and time resolutions of about 0.42 mm and 1 ns were achieved, respectively. This work serves as a benchmark for the development of a large-scale array of detectors for experiments with fission and fission-like reaction products at the Facility for Rare Isotope Beams (FRIB).

Instruments & Instrumentation↗

DOE OSTI2025

Assessing High Burnup U-19Pu-10Zr Fuel Performance against Historical and Modeled Behavior

Advancing the deployment of sodium-cooled fast reactors (SFRs) requires thorough testing of metallic fuel pins under accident conditions to establish safe operational limits of high burnup fuel. To conduct transient testing, a comprehensive understanding of steady-state fuel behavior obtained through both experimental characterization and accurate predictive capabilities is needed. This study comparatively assesses the steady-state irradiation performance of two high burnup U-19Pu-10Zr fuel pins, DP-36 and DP-40, irradiated under prototypic fast reactor conditions in preparation for planned safety testing at the Transient Reactor Test Facility. Since DP-40 was designated for use in the test and DP-36 serves as its sibling pin, non-destructive, engineering-scale post-irradiation examinations (PIE) were conducted on both pins while destructive examinations were performed exclusively on DP-36. The results were then assessed against historical performance data from similar fuel pins irradiated in the Experimental Breeder Reactor-II. Additionally, the steady-state irradiation of each pin was modeled using the BISON fuel performance code to assess the accuracy of current modeling capabilities in predicting the baseline irradiation behavior. Non-destructive examinations included neutron radiography to measure fuel column elongation, gamma scanning to verify pin integrity and fission product migration, and profilometry to assess dimensional changes. Benchmarking against existing PIE data revealed consistent patterns in axial fuel column growth and cladding diametral strain, though both pins exhibited longer low-density “fluff” structures, which can have implications for core reactivity and source term calculations. Destructive examinations on DP-36 included fission gas release analysis and sectioning for optical microscopy, which showed more complex constituent redistribution patterns than the traditionally accepted 3-ring model. The axial evolution of fractional areas and porosities of each of the redistributed zones were quantified and presented. Modeling comparisons showed agreement in fractional fission gas release but consistently overestimated axial and radial swelling and disagreed with measured axial porosity patterns. These conservative overpredictions suggested that the pins would appear closer to failure or operational limits at the start of transient tests, potentially leading to higher strain accumulation during the transient. While conservative estimates provide safety margins, they can negatively impact fuel economics. A review of the swelling models identified areas for improvement in the gaseous swelling, solid swelling, and fuel hot-pressing models when applied to ternary fuel. The results of this study highlight the critical importance of conducting pre-test characterization on both test and sibling pins to accurately capture steady-state fuel behavior, providing a precise baseline for post-test evaluations and essential inputs for transient modeling of the planned experiments. The analysis also revealed significant data gaps that require further investigation to enhance the understanding and prediction of fuel swelling and pore dynamics. Collecting comprehensive data across different irradiation conditions, burnup levels, and fuel compositions are essential for refining existing models and developing mechanistic models for both binary and ternary metallic fuels, ultimately improving the integration of modeling and experimental approaches in accident testing.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Hybrid Sorbents for 129 I Capture from Contaminated Groundwater

Radioiodine ( 129 I) poses a risk to the environment due to its long half-life, toxicity, and mobility. It is found at the U.S. Department of Energy Hanford Site due to legacy releases of nuclear wastes to the subsurface where 129 I is predominantly present as iodate (IO 3 – ). To date, a cost-effective and scalable cleanup technology for 129 I has not been identified, with hydraulic containment implemented as the remedial approach. Here, novel high-performing sorbents for 129 I remediation with the capacity to reduce 129 I concentrations to or below the US Environmental Protection Agency (EPA) drinking water standard and procedures to deploy them in an ex-situ pump and treat (P&T) system are introduced. Furthermore, this includes implementation of hybridized polyacrylonitrile (PAN) beads for ex-situ remediation of IO 3 – -contaminated groundwater for the first time. Iron (Fe) oxyhydroxide and bismuth (Bi) oxyhydroxide sorbents were deployed on silica substrates or encapsulated in porous PAN beads. In addition, Fe–, cerium (Ce)–, and Bi–oxyhydroxides were encapsulated with anion-exchange resins. The PAN–bismuth oxyhydroxide and PAN–ferrihydrite composites along with Fe- and Ce-based hybrid anion-exchange resins performed well in batch sorption experiments with distribution coefficients for IO 3 – of >1000 mL/g and rapid removal kinetics. Of the tested materials, the Ce-based hybrid anion-exchange resin was the most efficient for removal of IO 3 – from Hanford groundwater in a column system, with 50% breakthrough occurring at 324 pore volumes. The functional amine groups on the parent resin and amount of active sorbent in the resin can be customized to improve the iodine loading capacity. These results highlight the potential for IO 3 – remediation by hybrid sorbents and represent a benchmark for the implementation of commercially available materials to meet EPA standards for cleanup of 129 I in a large-scale P&T system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Hybrid Quantum–Classical Graph Transformers for Efficient Sentiment Analysis

Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT), a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self-attention. The QGT models variable-length sentences as token graphs, where both the embedding encoding and the self-attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and 𝑘 -nearest-neighbor graph structures and evaluate it on five benchmark sentiment-classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving 29.4 × fewer parameters while requiring 3–5 × fewer samples to reach comparable performance. These findings highlight the potential of graph-based quantum models as scalable and data-efficient architectures for natural language understanding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Development of a coupled experimental–computational approach for engineering optimization of spout-fluidized bed particle coating systems

The design of spout-fluidized bed (SFB) coating systems for nuclear particle fuels typically relies on trial-and-error processes, comprising iterative and time-consuming coating deposition experiments and post-deposition characterization. At an engineering scale, this approach to guided SFB system design is inefficient, highlighting the need for streamlined experimental methodologies which can correlate fluidization conditions to downstream coating outcomes. In this study, we combine time-resolved particle image velocimetry (PIV) with CFD–DEM simulations to benchmark hydrodynamic behavior in a 3D spout-fluidized bed. By exploiting easily accessible optical measurements of particle motion at the bed wall and within the spouting region, we obtain quantitative velocity fields that can be directly compared with model predictions of the occluded bed region, without resorting to complex imaging and characterization techniques such as X-ray or magnetic resonance tomography. Experimental benchmarking reveals strong agreement between CFD–DEM and PIV in the spout and annulus regions, while discrepancies near the wall highlight areas for future model development. Here, the proposed integrated experimental–numerical framework will enable a direct connection between measured variables and numerically predicted fluidization performance of dense, surrogate nuclear particle fuel feedstock such that experimental SFB component design can be rapidly evaluated, informing design decisions for nozzle geometry and operating conditions. Future work will extend this framework by correlating quantified fluidization metrics across nozzle geometries and operating conditions with the resulting coating morphology, microstructure, and uniformity. Establishing these correlations will enable predictive links between hydrodynamic performance and coating quality, providing a rational, scalable basis for optimizing SFB design prior to coating deposition.

CFD/DEM↗

DOE OSTI2025

Computation of Auger Electron Spectra in Organic Molecules with Multiconfiguration Pair-Density Functional Theory

Efficient and accurate computation of molecular Auger electron spectra for larger systems is limited by the rapid increase in the number of doubly ionized final states as the system size grows. Here, in this work, we benchmark the application of multiconfiguration pair-density functional theory with a restricted active space (RAS) reference wave function for computing the carbon K-edge decay spectra of 20 organic molecules. Decay rates are computed within the one-center approximation. We evaluate the performance of different basis sets and on-top functionals and find that multiconfiguration pair-density functional theory achieves accuracy comparable to RAS followed by second-order perturbation theory, but at significantly lower computational cost.

Fouda, Adam E. A. [Argonne National Laboratory (AN↗