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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗
DOE OSTI2022

Riverine Plastic Pollution: Sampling and Analysis Methods

Riverine plastic pollution has been found in all major U.S. rivers, but the exact amount of plastic being released to the oceans has not been quantified. Field studies conducted in U.S. rivers have used a range of sampling and analysis techniques and rarely measured the mass of the plastic collected. Measurements of riverine plastic pollution are needed to calibrate and validate models used to estimate the U.S. riverine plastic emissions to the oceans. This report surveys measurement methods used to quantify riverine pollution and current estimates of U.S. riverine plastic pollution from measurements and models. Measurement methods include field sampling and laboratory analysis. Field sampling methods are described for large (macro) and small (micro) plastic particles. Laboratory analysis methods are described for macro and microplastic with an emphasis on the detailed characterization processes of microplastics. Waterborne leachate analysis is also briefly described. Three models are described that estimate plastic pollution based on mismanaged plastic waste in the river catchment basins. The models were validated and calibrated with global data sources. The data sources were predominantly outside of the U.S., where the magnitude and composition of plastic pollution is different than what is found in U.S. rivers. Comprehensive measurements of riverine plastics are needed not only to characterize the riverine plastic pollution, but also parameterize and validate models of plastic fate and transport. This report also describes five key U.S. rivers that span a range of sizes and environmental conditions that could be sampled to obtain data to support characterization and model development of plastic pollution from rivers to oceans. Sampling and analysis protocol recommendations are made to ensure the highest quality of data are collected in the five rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigation of process history and underlying phenomena associated with the synthesis of plutonium oxides using Vector Quantizing Variational Autoencoder

Accurate, high throughput, and unbiased analysis of plutonium oxide particles is needed for analysis of the phenomenology associated with process parameters in their synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology through scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. Among other candidates, a neural network called a Vector Quantizing Variational Autoencoder (VQ-VAE) has shown the ability to quantitatively describe particle morphology to attain >85% accuracy in identifying uranium oxide processing routes. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO 2 ) particles created in a designed experiment and investigate their phenomenology and prediction of their process parameters. PuO 2 was calcined from Pu(III) oxalates that were precipitated under varying synthetic conditions that related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order; the surface morphology of the resulting PuO 2 powders were analyzed by SEM. A pipeline was developed to extract and quantify useful image representations for individual particles with the VQ-VAE, then further reduce the dimensionality of the feature space using a bottlenecking neural network fit to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters with greater than 80% accuracies for some parameters with a single particle. They also showed utility for grouping particles with similar surface morphology characteristics together. Both the clustering and classification results reveal valuable information regarding which chemical process parameters chiefly influence the PuO 2 particle morphologies: strike order and oxalic acid feedstock. Doing the same analysis with multiple particles was shown to improve the classification accuracy on each process parameter over the use of a single particle, with statistically significant results generally seen with as few as four particles in a sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Ion irradiation and modification: The role of coupled electronic and nuclear energy dissipation and subsequent nonequilibrium processes in materials

Understanding material responses to energy deposition from energetic charged particles is important for defect engineering, ion-beam processing, ion-beam analysis and modification, geologic aging, space exploration, and nuclear applications. As an incident ion penetrates a solid, its energy is transferred to electrons and to atomic nuclei of the solid. Much of this electronic energy deposition is subsequently transferred to the atomic structure via electron–phonon (e–ph) coupling, leading to local inelastic thermal spikes in which energy dissipation is influenced by the local environment. In addition, intense ionization can lead to high densities of localized electronic excitations in wide-bandgap materials and ceramics that can affect defect dynamics and atomic mobility. Specifically, energy exchange between electrons and atomic nuclei, along with localized electronic excitations, can lead to substantial competitive (ionization-induced annealing), additive (both electronic and nuclear energy depositions contributing to damage production), and synergistic (more damage than the sums of separate damage processes) effects. Although nonmonotonic effects of the e–ph coupling strength and athermal processes are demonstrated for pre-existing defects and residual damage during ion–solid interactions, there is limited understanding of when such electronic effects must be considered in atomic-scale models of damage production and evolution in a broad variety of materials. Complex ceramics and chemically disordered solid solution alloys with different constituent elements allow a systematic evaluation of defect dynamics and irradiation performance with increasing complexity. Current knowledge regarding tuning of bonding characteristics and chemical disorder to control atomic-level dynamics is reviewed. Although a lack of fundamental understanding obstructs the advancement of reliable predictions for ion beam material modification, it highlights challenges and opens research opportunities. Insights into the complex electronic and atomic correlations with extreme energy deposition will strengthen our ability to design materials and predict ion-irradiation-induced damage in a radiation environment, and they may pave the way to better control fundamental processes and design new material functionalities for advanced technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Novel Process for Carbon Dioxide Conversion to Fuel

In this project, TDA developed a new mixed metal oxide-based sorbent that converts CO2 (captured from coal fired power plants) to CO, which can then be combined with renewable H2 generated by water electrolysis or H2 from steam methane reforming to produce different liquid fuels. TDA’s absorbent-based CO2 conversion process uses a redox process, which splits the catalytic reforming of methane with CO2 reaction into two stages: CO2 reduction to CO and CH4 reforming into H2 and CO which eliminates the equilibrium limitations. The CO produced in the two-stage reactor system can then be further reacted with renewable H2 to produce methanol, naphtha, diesel, or gasoline. We worked with the Advanced Power & Energy Program (APEP) of University of California, Irvine (UCI) to design and develop the liquid fuel synthesis process that is built around this new material. We demonstrated the techno-economic viability of the new sorbent based redox process to convert CO2 into synthesis gas by demonstrating continuous carbon dioxide reduction in a prototype test system for over 585 hours while converting up to 10 kg CO2/day. With the successful completion of the R&D effort, the technology is now ready for a larger pilot-scale demonstration and the technology readiness has been raised from TRL 3 to TRL 5. In collaboration with UCI, we completed a high-fidelity process design and economic analysis. The required sale price (RSP) for gasoline (Case 1 NG-MTG) is $4.91/gal and naphtha and diesel (Case 2 NG-FT) are $4.23/gal and $6.07/gal, respectively, on a 2011 dollar basis. To put these costs in perspective, the California prices in current dollars (with its strict specifications) for regular grade gasoline from last year to current year have varied from a low of $3.10/gal in January 2021 to a high of $5.76/gal in March 2022, while prices for diesel from last year to current year have varied from a low of $3.40/gal in January 2021 to a high of $6.41/gal in May 2022 according to the U.S. Energy Information Agency data. It should be noted that the gasoline and diesel produced by these designs of Case 1 and Case 2 would be of very high quality and both nitrogen and sulfur free. These RSPs are based on a cost of imported electricity of $64/MWh based on the low-end current wind generated electricity cost (Genevieve 2011). This cost is by far the largest component of the variable costs used in computing the RSPs. A sensitivity analysis of these RSPs to the cost of imported electricity shows that the cost of the imported electricity has a significant effect on the RSPs. The life cycle analysis (LCA) shows that the total cradle-to-gate CO2 emissions for both liquid fuels (diesel and gasoline) were negative, indicating that overall more CO2 is consumed than released during production of the fuel from CO2 feed stack for both cases (-296 kgCO2 per MT gasoline for Case 1 and -705 kgCO2 per MT diesel for Case 2). On a cradle-to-grave comparison, the use of diesel produced using TDA’s process (2,457 kgCO2 per MT diesel) would release 37.6% less CO2 compared to petroleum based diesel (3,937 kgCO2 per MT diesel) while the use of gasoline produced using TDA’s process (2,792 kgCO2 per MT gasoline) would release 29.3% less CO2 compared to petroleum based gasoline (3,946 kgCO2 per MT gasoline). With the successful completion of the R&D effort, the technology is now ready for a bench-scale demonstration and the technology readiness has been raised from TRL 3 (Analytical and experimental critical function and/or characteristic proof of concept) to TRL 5 (Laboratory scale similar system validation in relevant environment).

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Membrane Adsorbents Comprising Self-Assembled Inorganic Nanocages (SINCs) for Super-fast Direct Air Capture Enabled by Passive Cooling

The objectives of the proposed project were to develop highly porous membrane adsorbents comprising CO 2 -philic polymers and self-assembled inorganic nanocages (SINCs) for rapid temperature swing adsorption using electricity-free solar heating and radiative cooling, enabling an economically viable approach for direct air capture (DAC). Our core technical activities combine three key innovations. (1) Highly porous flat-sheet membrane adsorbents contain CO 2 -philic amines that can be easily produced using a phase inversion method. (2) CO 2 -philic SINCs can be easily dispersed in the polymers with great stability (compared with the metal-organic frameworks or MOFs). (3) The adsorption and desorption are integrated with solar heating and radiative cooling for rapid continuous operation, in contrast to traditional long-cycle separate operation. The membrane adsorbents containing amines, polymers, and SINCs were produced using a one-step industrial process. The porous membranes coupled with porous SINCs offer low resistance for gas flow and fast CO 2 sorption/desorption cycles, while the incorporation of the additional amine groups provides high CO 2 sorption capacity. The key achievements are summarized below. (1) Membrane adsorbents with high PEI loading (>40%), high porosity of >80%, and low gasflow resistance were prepared in one step using commercially available, low-cost materials. (2) Membrane adsorbents based on Solupor and PEI show CO 2 sorption capacity of >1.5 mmol/g using air containing 400 ppm at a relative humidity of 15%. (3) Effect of the adsorbent compositions (such as PEI type, PEI content, SINC content, porosity) on the CO 2 sorption was systematically investigated. (4) Effect of the processing conditions (such as CO 2 content, temperature, and relative humidity) on the CO 2 sorption was systematically investigated; (5) The stability of the membrane adsorption against many cycles of sorption and desorption was studied. The higher molecular weight of PEI (PEI25k) shows better stability than PEI800. (6) Advanced materials with radiative cooling were developed, which can decrease the temperature by 5-7 °C compared to the ambient temperature. (7) Preliminary techno-economic analysis shows that our process may achieve a capture cost of $1,343/tonne CO 2 with a total OPEX cost of $1,112/tonne CO 2 . The adsorbent replacement cost accounted for 52% of the total OPEX cost. Membrane adsorbents with lower costs and longer operation life can significantly decrease the cost. The proposed project directly addresses the requirement of DE-FOA-0002188, i.e., novel materials with CO 2 adsorption capacity for direct air capture with integrated solar heating and radiative cooling to reduce the cost of the DAC. Our future work will focus on the development of low-cost adsorbents that can be stable at the sorption and desorption conditions for long term.

14 SOLAR ENERGY↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A small-volume, high-throughput approach for surface tension and viscosity measurements of liquid fuels

An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Illinois Storage Corridor CarbonSAFE Phase III: Stakeholder Engagement and Outreach Plan

The Stakeholder Engagement and Outreach Plan provides a comprehensive framework for engaging stakeholders of the Illinois Storage Corridor (ISC) project. The ISC project is a CarbonSAFE Phase III project designed to facilitate commercial deployment of carbon capture, utilization, and storage (CCUS) in Illinois. The project aims to establish a multi-industry carbon storage corridor through development of storage sites near the One Earth Energy (OEE) ethanol production facility in north-central Illinois and the Prairie State Generating Company (PSGC) coal-fired power plant in south-central Illinois, with combined annual CO 2 capture ultimately exceeding 8.6 million tons per year. Stakeholder engagement is recognized as a critical component for successful CCUS deployment, alongside technical and economic considerations. As an emerging technology, CCUS may not be well understood by the general population, and lack of public awareness can lead to opposition that poses significant barriers to project development. This plan addresses this challenge through systematic stakeholder identification, analysis, planning, and implementation of engagement actions. The plan is structured around four main sections: Communication, Stakeholder Analysis, Stakeholder Engagement, and Environmental Justice. Activities will be conducted under Tasks 1 and 4 of the project's Statement of Project Objectives, with two key subtasks: (1) developing a stakeholder analysis and engagement plan through face-to-face meetings, facilitated discussions, and surveys; and (2) implementing stakeholder engagement and public outreach activities including meetings, open houses, and permit hearings. The Illinois State Geological Survey (ISGS) will manage engagement activities following DOE-NETL best practices, focusing on providing objective, fact-based information about CCUS and the ISC project. A comprehensive Communication Plan establishes protocols for media contacts, site visits, and crisis communications. The stakeholder analysis follows a structured workflow process divided into Pre-feasibility and Feasibility phases, incorporating contextual understanding, assessment, data collection, and analysis. Key stakeholder groups include government bodies, educational organizations, conservation and environmental groups, agricultural communities, and religious organizations. The plan addresses common stakeholder questions regarding project risks, benefits, safety, property values, liability, and environmental impacts. Recommendations emphasize developing clear messaging, creating informational materials, and preparing to address both project-specific and broader environmental concerns to ensure transparent communication and build stakeholder support throughout project implementation.

25 ENERGY STORAGE↗
DOE OSTI2023

Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning

Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. Here, this new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast and Universal Kohn-Sham Density Functional Theory Algorithm for Warm Dense Matter to Hot Dense Plasma

Understanding many processes, e.g., fusion experiments, planetary interiors, and dwarf stars, depends strongly on microscopic physics modeling of warm dense matter and hot dense plasma. This complex state of matter consists of a transient mixture of degenerate and nearly free electrons, molecules, and ions. This regime challenges both experiment and analytical modeling, necessitating predictive ab initio atomistic computation, typically based on quantum mechanical Kohn-Sham density functional theory (KS-DFT). However, cubic computational scaling with temperature and system size prohibits the use of DFT through much of the warm dense matter regime. A recently developed stochastic approach to KS-DFT can be used at high temperatures, with the exact same accuracy as the deterministic approach, but the stochastic error can converge slowly and it remains expensive for intermediate temperatures (< 50 eV). Here we have developed a universal mixed stochastic-deterministic algorithm for DFT at any temperature. This approach leverages the physics of KS-DFT to seamlessly integrate the best aspects of these different approaches. We demonstrate that this method significantly accelerated self-consistent field calculations for temperatures from 3 to 50 eV, while producing stable molecular dynamics and accurate diffusion coefficients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Reaction processes at step edges on S-decorated Cu(111) and Ag(111) surfaces: MD analysis utilizing machine learning derived potentials

We report a variety of complexation, reconstruction, and sulfide formation processes can occur at step edges on the {111} surfaces of coinage metals (M) in the presence of adsorbed S under ultra-high vacuum conditions. Given the cooperative many-atom nature of these reaction processes, Molecular Dynamics (MD) simulation of the associated dynamics is instructive. However, only quite restricted Density Functional Theory (DFT)-level ab initio MD is viable. Thus, for M = Ag and Cu, we instead utilize the DeePMD framework to develop machine-learning derived potentials, retaining near-DFT accuracy for the M–S systems, which should have broad applicability. These potentials are validated by comparison with DFT predictions for various key quantities related to the energetics of S on M(111) surfaces. The potentials are then utilized to perform extensive MD simulations elucidating the above diverse restructuring and reaction processes at step edges. Key observations from MD simulations include the formation of small metal–sulfur complexes, especially MS 2 ; development of a local reconstruction at A-steps featuring an S-decorated {100} motif; and 3D sulfide formation. Additional analysis yields further information on the kinetics for metal–sulfur complex formation, where these complexes can strongly enhance surface mass transport, and on the propensity for sulfide formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗
DOE OSTI2020

NEAMS Workbench Status and Capabilities

The mission of the US Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished by using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to highfidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering-scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process can enable multifidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences compared with native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed herein.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Coal-based Bricks & Blocks (CBBs): Process Development to Prototype Fabrication Coupled with Techno-Economic Analysis and Market Survey

The decline of coal use for energy production provides an abundance of local feedstock for new innovative uses and value-added products. Expanding the U.S. coal-value chain to manufacture high-value carbon products can strengthen the nation’s energy and mineral security, enhance the U.S. national defense security, increase the United States’ economic prosperity, while achieving U.S. environmental objectives. The primary project goal is a relatively light-weight composite product with superior (or comparable) compressive strength. Both virgin and, where available, post-consumer recycled thermoplastic versions are tested for each thermoplastic species. A useful attribute of thermoplastics as binders is that they can be heated to their melting point, cooled, and reheated again without significant degradation. A key advantage of thermoset CBBs is that they require only mixing and molding. CBB advantages include low cost, availability, binding ability and processability. Coal-based bricks and blocks (CBBs) weigh about 50% less than clay bricks and can be manufactured with an interlocking design to promote ease of use for the novice builder. CBB formulation is evaluated according to a design-of-experiments (DoE) approach. DoE variables are a) relative weight fractions of binder, b) relative proportions of large versus small (milled) anthracite size fractions, and c) additive percentage. Fabrication methods include hot-press molding and extrusion, the later being the most commercially viable. CBBs are tested for compressive strength, modulus of rupture (by flexure test) and water absorption per ASTM C67, with density determined by the Archimedes drainage method. Fractured interfaces are examined by SEM (Scanning Electron Microscopy) to resolve fracture dynamics and interior microstructure uniformity. Differential scanning calorimetry (DSC) is used to compare plastic transition temperatures i.e., glass and melting temperatures to contrast virgin with post-consumer recycled thermoplastics and optimize their usage. These results are used in the DoE analysis to identify the binder and relative weight percentages for optimum strength, density, and porosity. Overall, CBBs possess strength comparable to clay-based bricks but are non-permeable and hydrophobic, and hence resistant to degradation by freeze-fracturing, corrosion, and efflorescence. The strongest composites have been made with the following thermoplastic binders (in order of strength): thermoset, high-density polyethylene crosslink resin, high-density polyethylene, nylon 6/6, and polypropylene. Results from a techno-economic analysis TEA show economy of scale for CBBs by modularization and reveal the binder as the cost driver for material costs. Ideally, the incorporation of post-consumer recycled thermoplastic will decrease material acquisition costs and increase product sustainability. Notably, CBBs do not require the high temperature calcination needed to produce cement, nor do they require firing in the 1600-2400 °F range for three days using natural gas, as do clay brick equivalents. Instead, CBBs are heated to a modest <600 °F according to the melt flow index of the thermoplastic binder. Existing anthracite mines can be expanded to produce CBBs to reduce aggregate transportation costs and emissions that exist for clay bricks. TEA reflects this reduced energy cost while a comparative CO2 emission analysis quantifies the reduced environmental footprint. The market survey identifies several commercialization opportunities, dependent upon the brick classification.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Understanding Model Inadequacy in TRISO Nuclear Fuel Fission Products Release Models: Empirical and Mechanistic Approaches

The increasing use of tristructural isotropic (TRISO) particle fuel in both advanced and existing reactors necessitates a thorough evaluation of uncertainties and shortcomings in TRISO fission product release models. These inadequacies arise from the simplifications made in computational models compared to experimental data. Utilizing the BISON fuel performance code and experimental data from the Advanced Gas Reactor (AGR) program provides a unique chance to rigorously assess these inadequacies within a Bayesian uncertainty quantification (UQ) framework. This study contrasts the standard Bayesian framework with the Kennedy-O'Hagan (KOH) framework, which explicitly accounts for modeling inadequacies, in the context of UQ for TRISO silver release models. It examines both the traditional Arrhenius equation and a more advanced lower-length-scale (LLS)-informed model that incorporates microstructure information. The inverse UQ process applied to AGR-2 and AGR-3/4 datasets identified modeling inadequacy as the primary source of uncertainty, with experimental noise also being significant, while model parameter uncertainty was minimal. Both the Arrhenius and LLS-informed models showed similar levels of modeling inadequacy. For forward predictive UQ using the AGR-1 dataset, the KOH framework enhanced the accuracy and quality of quantified uncertainties by approximately 30% and 40%, respectively, compared to the standard Bayesian framework. This improvement was observed for both the Arrhenius and LLS-informed models. At the engineering scale, both models performed similarly, but the LLS-informed model outperformed the Arrhenius equation at the mesoscale. These findings underscore the importance of explicitly considering modeling inadequacy in the UQ process and highlight the need for ongoing refinement of physics-based models to address these shortcomings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗