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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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Raw data for Figure 2 is located in: /Experiments/Fig2_delayed_aggregation Raw data for Figures 3 and 4 can be found at: /Experiments/Fig3_4_Core_shell The analysis code is available at: /Experiments/Code Raw data for the Figure 5 simulation is locate in: /Simulation/Raw_data/Fig5_Structural_features_of_cluster
Preliminary planning for retrieval, qualification, and pretreatment of waste in Hanford’s 200 West Area (200W) has begun as part of the West Area Risk Management project. Experimental studies to technically mature pretreatment process operations will likely be needed because of the uniqueness of 200W waste. Pacific Northwest National Laboratory formulated five simulants to represent 200W-qualified feed based on the preliminary flowsheet provided by Washington River Protection Solutions, LLC. The simulant recipes were devised using applicable historical information as a reference point to support the use of the flowsheet waste vectors, which were combined into five distinct groups. These five groups formed the basis for the liquid composition targets that were adapted into recipes using charged-balanced salt species. The liquid phase recipes were batched in 1-L quantities and analyzed at Pacific Northwest National Laboratory. Once confirmed to be stable, the liquid solutions were tested for compatibility with candidate solid components. Specific solid components were recommended based on cross-examining the proposed solid phases in the flowsheet with relevant data from the literature. Mixtures of solid components were added to aliquots of the liquid batches and sub-sampled to measure particle size distribution. The measured distribution was compared to independently created benchmark distributions appropriate for each simulant. This process was iterated until a solid phase composition that resulted in a representative particle size distribution was found. After the final compositions were confirmed, a suite of chemical and physical characterization data was collected. This report describes the simulant basis, formulation methodology, laboratory measurements, and data collected for the recipes recommended to represent 200W waste feeds.
Gold nanoparticle synthesis recipes were extracted from the literature to obtain data-driven hypotheses for synthesis outcome morphology and size. Used images from https://Flaticon.com.
Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.
Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.
HPC ODA Commons is a community-driven platform for standardizing HPC operational data analytics. HPC sites generate enormous volumes of operational data - scheduler logs, accounting records, monitoring streams - but turning that data into actionable insight is needlessly hard. Each site builds bespoke parsers, schemas, and evaluation pipelines. Results can't be compared across institutions. Promising analytics ideas stay siloed because there's no shared language for describing the data, the experiments, or the outcomes. HPC ODA Commons fixes this by establishing community-governed contracts - versioned schemas, canonical artifacts, and benchmark recipes - that make ODA workflows discoverable, reproducible, and comparable. It pairs these standards with a practical, CLI-first toolkit that lets operators and researchers go from raw logs to standardized results without sending data off-cluster.
The objective of this DOE-funded project by the Electric Power Research Institute, Inc. (EPRI), Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), is to qualify coal, biomass, and plastic waste blends based on performance testing of selected pellet recipes in a pilot-scale updraft moving-bed gasifier. The testing provides relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of the waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are a focus. The gasifier is Hamilton Mauer International, Inc. (HMI)’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips. However, mixtures of these fuels with plastic wastes had not yet been prepared and gasified together. The feedstocks were prepared by California Pellet Mill (CPM) under contract to HMI.
The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently processing electronic polymer solutions into thin films with specific properties remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore processing pathways for achieving high-conductivity, low-defect electronic polymers films. Leveraging importance-guided Bayesian optimization, Polybot efficiently navigates a complex 7-dimensional processing space. In particular, the automated workflow and algorithms effectively explore the search space, mitigate biases, employ statistical methods to ensure data repeatability, and concurrently optimize multiple objectives with precision. The experimental campaign yields scale-up fabrication recipes, producing transparent conductive thin films with averaged conductivity exceeding 4500 S/cm. Feature importance analysis and morphological characterizations reveal key design factors. This work signifies a significant step towards transforming the manufacturing of electronic polymers, highlighting the potential of AI-driven automation in material science.
The Verifiable Fuel Cycle Simulation (VISION) is a system-dynamics model of the entire nuclear fuel cycle, from mining to disposal, using user-defined deployment scenarios, fuel recipes, and recycling parameters. It is written in the PowerSim simulation environment and utilized MS Excel to manage data inputs and outputs.
The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.
High-energy neutrinos arise from processes at large center-of-mass energies, offering a window to test physics at comparable scales or beyond those accessible in collider experiments on Earth. Here, we present a recipe for extracting two-sided bounds on the inelastic $pp$ and $pγ$ cross sections from neutrino point-source data, by independently constraining every astrophysical input (cosmic-ray luminosities and target densities) through electromagnetic observations or theoretical arguments. The cross section is then the only remaining free parameter. Applying this framework to the IceCube associations with TXS~0506+056, NGC~1068, and the Galactic Plane, to a stacked population of eleven X-ray bright Seyfert galaxies, to the ultra-high-energy KM3NeT event KM3-230213A, and to projected observations of ultra-high-energy neutrinos, we obtain constraints that span center-of-mass energies from $\sqrt{s}\sim 1$ GeV to $\sim 10^{5}$ GeV, some of which are well beyond the reach of the LHC and, for the $pγ$ channel, beyond HERA. Several of these bounds are more stringent than unitarity limits.
Life cycle inventory (LCI) data are critical for robust life cycle assessment (LCA), yet many widely used datasets such as the U.S. Life Cycle Inventory (USLCI) are not natively compatible with advanced modeling frameworks like Brightway2. This work presents an automated pipeline to transform USLCI data into a fully functional Brightway2 project. The workflow performs systematic data cleaning, resolves duplicate process and exchange identifiers, and applies allocation to multi-output processes. Technosphere and biosphere flows are harmonized through unit conversions and a bridge mapping to the biosphere3 database, with comprehensive logging of missing flows and cutoff issues. The resulting Brightway2 database is validated using matrix diagnostics to ensure consistency of the technosphere, and is benchmarked via life cycle impact assessment (LCIA) methods such as ReCiPe and IPCC GWP. Outputs include reproducible CSV exports of corrected processes, elementary flows, characterization factors, and LCIA results, alongside backup utilities for project sharing. This pipeline lowers barriers for integrating USLCI data into open-source LCA workflows, enabling reproducible, validated LCA inventories within the Brightway 2 framework.
The Parallel Refinement and Integration System for Multi-azimuthal Analysis (PRISMA, version 1.1.0) is a Python application for processing X-ray diffraction (XRD) image data. PRISMA wraps GSAS-II to perform azimuthally-binned peak refinement, computes per-frame strain and d-spacing from those fits, and provides three PyQt5 graphical interfaces: (1) a Recipe Builder for selecting GSAS-II control (.imctrl) files, optional mask (.immask) files or threshold-ased masking, reference and experiment image sets, peaks, zimuthal range and bin size, and an optional ceria-based auto-calibration; (2) a Batch Processor that uses Dask on local workstations and pure MPI (mpi4py.futures.MPICommExecutor) on HPC to distribute GSAS-II refinement across cores or compute nodes and write results to a 4-dimensional (peaks x frames x azimuths x measurements) Zarr dataset; and (3) a Data Analyzer that renders heatmaps of fit parameters, strain, frame-to-frame deltas, and percent-change-vs-reference, and exports user-defined subsections to CSV or Excel. The peak-refinement algorithm is deterministic. Benchmark on ALCF Crux: a 20,000-image set, single-peak fit in frame mode with 44 azimuthal bins on 128 nodes x 128 workers, 48 seconds total wall time.
BiFeO 3 (BFO) is a next-generation non-toxic multiferroic material with applications in sensors, memory devices, and spintronics, where its crystallinity and crystal structure directly influence its functional properties. Designing sol–gel syntheses that result in phase-pure BFO remains a challenge due to the complex interactions between metal complexes in the precursor solution. Here, we combine text-mined data and chemical reaction network (CRN) analysis to obtain novel insight into BFO sol–gel precursor chemistry. We perform text-mining analysis of 340 synthesis recipes with the emphasis on phase-pure BFO and identify trends in the use of precursor materials, including that nitrates are the preferred metal salts, 2-methoxyethanol (2 ME) is the dominant solvent, and adding citric acid as a chelating agent frequently leads to phase-pure BFO. Our CRN analysis reveals that the thermodynamically favored reaction mechanism between bismuth nitrate and 2ME interaction involves partial solvation followed by dimerization, contradicting assumptions in previous literature. We suggest that further oligomerization, facilitated by nitrite ion bridging, is critical for achieving the pure BFO phase.
New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO 3 , CaZrN 2 , and YMnO 3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico .
The Dark Energy Spectroscopic Instrument (DESI) will provide precise measurements of Baryon Acoustic Oscillations (BAO) to constrain the expansion history of the Universe and set stringent constraints on dark energy. Therefore, precise control of the global error budget due to various systematic effects is required for the DESI 2024 BAO analysis. In this work, we estimate the level of systematics induced in the DESI BAO analysis due the assumed Halo Occupation Distribution (HOD) model for the Emission Line Galaxy (ELG) tracer. We make use of mock galaxy catalogs constructed by fitting various HOD models to early DESI data, namely the One-Percent survey data. Our analysis includes typical HOD models for the ELG tracer used in the literature as well as extensions to the baseline models. Among the extensions, we consider various recipes for galactic conformity and assembly bias. We use 25 AbacusSummit simulations under the ΛCDM cosmology for each HOD model and perform independent analyses in Fourier space and in configuration space. To recover the BAO signal from our mocks we perform BAO reconstruction and apply the control variates technique to reduce sample variance noise. Our BAO analyses can recover the isotropic BAO parameter α iso within 0.1% and the Alcock Paczynski parameter α AP within 0.3%. Overall, we find that the systematic error due to the HOD dependence is below 0.17%, with the Fourier space analysis being more robust against the HOD systematics. We conclude that our analysis pipeline is robust enough against the HOD systematics for the ELG tracer in the DESI 2024 BAO analysis, for the assumptions made.
The potential to accelerate the processing of low- and high-level tank waste by applying real-time monitoring (RTM) of chemical and physical properties has prompted research into the suitability of multiple analytical methods for that purpose. The broad variety of waste stream properties and the large number of analytes of interest (as evidenced by Waste Acceptance Criteria (WAC) and Process Control Limit (PCL) lists) lead to an overwhelming set of possible analytical scenarios. This report describes the use of Best Basis Inventory (BBI) data to find the most relevant analytical targets for the specific case of monitoring the blending of High Level Waste from multiple tanks prior to introduction into a vitrification facility. Campaigns for blending this waste to minimize the risk of exceeding WACs and PCLs have been proposed. However, the predicted compositions of the blended materials do not incorporate any uncertainties that may be associated with the representativeness of the waste layer samples or the laboratory analyses that generated the BBI data. Also, they do not include any uncertainty associated with the precision of collecting highly specific fractions of the layers during a blending campaign or any inhomogeneities that may exist in those layers. Monte Carlo methods are used to apply uncertainties to the compositions of the individual layers specified in the campaign recipes. The resulting variations in the compositions of the blended materials allow estimation of the risks of exceeding WACs and PCLs for each campaign. A critical subset of WACs/PCLs – NOx, NaK, AlFeZr, and S – are especially at risk of being exceeded in multiple campaigns. These analytes should be the focus of instrument development. We also have extracted the expected solid/supernate distribution for these analytes, which establishes important performance criteria for individual analytical methods. The BBI data also permits an understanding of the different chemical forms in which the analytes appear. Thus, the need to establish instrumental sensitivity to these forms can be gainfully addressed. Although concentrating on one specific application – the blending of tank waste - this approach should be generalizable for the analysis of other possible RTM applications for waste processing.
Predicting a material’s thermal properties is not only useful for designing parts but also for defining processing parameters. This is especially applicable to wet-filament winding operations and the production of composite parts using thermoset res ins. For these, the formation of cross-linking bonds is affected by time and temperature, but the degree of cross-linking can be modeled using phenomenological methods. In the current study, the Kissinger and Flynn/Wall/Ozawa methods were both used to calculate kinetic parameters and produced values that were in agreement with each other. The kinetic parameters activation energy ( E a ) and pre-exponential factor (𝐴) calculated from exothermic peak data increased between an epoxy and resole phenol resin system, with a cyanate ester having the highest. The successful application of both methods suggests applicability to future studies for developing cure recipes.