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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Critical length scales for chemical heterogeneity at Cu/Nb 3D interfaces by atom probe tomography

Cu/Nb nanocomposites containing sharp, two-dimensional (2D) interfaces have outstanding strength but limited deformability. In contrast, Cu/Nb with three dimensional (3D) biphase interfaces exhibiting crystallographic, topological, and chemical variations in all spatial dimensions overcomes this limitation by simultaneously enhancing material strength and deformability. Here, while structural characterization of 3D interfaces has been performed to understand their mechanical behavior, three dimensional chemical characterization of such interfaces is lacking. In this work we quantify the local chemistry of 3D interfaces in Cu/Nb nanocomposites using atom probe tomography (APT). Our analysis demonstrates chemical heterogeneities along all spatial dimensions in 3D interfaces, establishes the length scale of such features, and quantifies the morphology of 3D interfaces. 3D interface heterogeneities form by surface diffusion during physical vapor deposition (PVD), suggesting that deposition parameters can be used to control interface structure and provide unique ways to explore processing-structure-property relationships in interface-dominated nanocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Guided Ion Beam and Quantum Chemical Investigation of the Thermochemistry of Thorium Dioxide Cations: Thermodynamic Evidence for Participation of f Orbitals in Bonding

Kinetic energy dependent reactions of ThO + with O 2 are studied using a guided ion beam tandem mass spectrometer. The formation of ThO 2 + in the reaction of ThO + with O 2 is observed to be slightly endothermic and also exhibits two obvious features in the cross section. These kinetic energy dependent cross sections were modeled to determine a 0 K bond dissociation energy of D 0 (OTh + - O) = 4.94 ± 0.06 eV. This value is slightly larger but within experimental uncertainty of less precise previously reported experimental values. The higher energy feature in the ThO 2 + cross section was also analyzed and suggests formation of an excited state of the product ion lying 3.1 ± 0.2 eV above the ground state. Additionally, the thermochemistry of ThO 2 + was explored by quantum chemical calculations, including a full Feller-Peterson-Dixon (FPD) composite approach with correlation contributions up to CCSDT(Q) and four-component spin-orbit corrections, as well as more approximate CCSD(T) calculations including semiempirical estimates of spin-orbit energy contributions. The FPD approach predicts D 0 (OTh + -O) = 4.87 ± 0.04 eV, in good agreement with the experimental value. Analogous FPD results for ThO+, ThO, and ThO 2 are also presented, including ionization energies for both ThO and ThO 2 . The ThO 2 + bond energy is larger than those of its transition metal congeners, TiO 2 + and ZrO 2 + , which can be attributed partially to an actinide contraction, but also to contributions from the participation of f orbitals on thorium that are unavailable to the transition metal systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Multifunctional textured graphene-based coatings on elastomeric gloves for chemical protection

Nanotechnology offers a variety of new tools for the design of next-generation personal protective equipment (PPE). One example is the use of two-dimensional materials as coatings that enhance the performance and ergonomics of elastomeric gloves designed to protect users from hazardous chemicals. Desirable features in such coatings may include molecular barrier function, liquid droplet repellency, stretchability for compatibility with the elastomer, breathability, and an ultrathin profile that preserves the user's manual dexterity and tactile sensation. The present work explores the potential of engineered graphene-based films with out-of-plane texturing as a novel platform to meet these multifold requirements. Graphene-based films in different formulations were fabricated from water-borne inks by vacuum filtration and solution casting methods on glove-derived nitrile rubber substrates. The various coatings were then subjected to tests of molecular permeation by model volatile organic compounds, droplet contact angle, breathability, and mechanical stability during stretching and solvent immersion. The films dramatically improve the barrier properties of glove-derived nitrile. The out-of-plane graphene texturing imparts stretchability through microscale folding/unfolding, while also enhancing droplet repellency in some cases through a lotus-like roughening effect. The combined results suggest that engineered textured graphene-based films are a promising platform for creating multifunctional coatings for a next generation of chemically protective gloves and other elastomer-based PPE.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Minimizing separatrix crossings through isoprominence

Abstract A simple property of magnetic fields that minimizes bouncing to passing type transitions of guiding center orbits is defined and discussed. This property, called isoprominence, is explored through the framework of a near-axis expansion. It is shown that isoprominent magnetic fields for a toroidal configuration exist to all orders in a formal expansion about a magnetic axis. Some key geometric features of these fields are described.

confinement↗
DOE OSTI2023

Nested solitons in two-field fuzzy dark matter

ABSTRACT Dark matter as scalar particles consisting of multiple species is well motivated in string theory where axion fields are ubiquitous. A two-field fuzzy dark matter (FDM) model features two species of ultralight axion particles with different masses, m1 ≠ m2, which is extended from the standard one-field model with $m_a \sim 10^{-22} \, {\rm eV}$. Here we perform numerical simulations to explore the properties of two-field FDM haloes. We find that the central soliton has a nested structure when m2 ≫ m1, which is distinguishable from the generic flat-core soliton in one-field haloes. However, the formation of this nested soliton is subject to many factors, including the density fraction and mass ratio of the two fields. Finally, we study non-linear structure formation in two-field cosmological simulations with self-consistent initial conditions and find that the small-scale structure in two-field cosmology is also distinct from the one-field model in terms of DM halo counts and soliton formation time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Graph reinforcement learning for exploring model spaces beyond the standard model

We present a methodology for performing scans of beyond the standard model (BSM) parameter spaces with reinforcement learning. We identify a novel procedure using graph neural networks that is capable of exploring spaces of models without the user specifying a fixed particle content, allowing broad classes of BSM models to be explored—in theory, the technique is applicable to nearly any model space with a prespecified gauge group. We provide a generic procedure by which a suitable graph grammar can be developed for any BSM model that features user-specified symmetry groups and a finite number of different possible particle species, the use of which is applicable to a variety of machine learning tasks over the actions of BSM theories beyond our particular reinforcement learning use case. As a proof of concept, we construct the graph grammar for theories with vectorlike leptons that may or may not be charged under a dark U ( 1 ) group, inspired by portal matter extensions of the sub-GeV vector portal/kinetic mixing simplified dark matter models. We then use this graph grammar to create a reinforcement learning environment tasked with creating models with these vectorlike leptons that are consistent with a list of a variety of precision observables. The reinforcement learning agent succeeds in developing models that can address the observed muon anomalous magnetic moment discrepancy while remaining consistent with flavor violation and electroweak precision observables, including both constructions that have previously been studied as well as new models that have not, to our knowledge, previously been identified. By inspecting the resulting ensembles of models that the agent produces and experimenting with different configurations for our reinforcement learning environment and graph grammar, we also infer various lessons about the development of these environments that can be transferable to reinforcement learning scans of more complicated model spaces and comment on future directions for the development of this technique into a more mature tool. Published by the American Physical Society 2025

Wojcik, George N.↗
DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

De-risking superhot EGS development through 3D play fairway analysis: Methodology development and application at Newberry Volcano, Oregon, USA

The DErisking Exploration for multiple geothermal Plays in magmatic ENvironments (DEEPEN) project aims to de-risk exploration of geothermal plays in magmatic systems, with a focus on superhot and supercritical geothermal systems. This article presents advancements in geoscientific assessment of superhot enhanced geothermal system (EGS) resources as part of the DEEPEN project, particularly in exploration and identification of these systems. While there exists a general consensus on key components of conventional hydrothermal systems, there has not been a prior consensus on such components for EGS, let alone superhot EGS. The DEEPEN project identified these key components for superhot EGS plays, and used them to modify the traditional geothermal play fairway analysis (PFA) approach to identify favorable areas in superhot EGS plays in 3D. This was done through modeling both favorability and uncertainty separately for each key component of a superhot EGS play, incorporating diverse 3D geoscientific datasets, including geologic features, models produced from direct observations, single inversions, and joint inversions. The PFA is applied to Newberry Volcano as a form of validation. Finally, the results appear to validate the methodology, aligning with expectations derived from conceptual modeling, and highlighting the area targeted for EGS stimulation well NWG 55-29 as favorable, and suggesting potential additional areas worthy of exploration at Newberry.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Electrifying education: Exploring the electrification potential of U.S. School bus fleets

We analyze the operations of 270 diesel school buses across the United States to assess their electrification potential and evaluate the impact of various charging strategies on electricity demand. We find that school buses typically follow a two-route schedule on weekdays, featuring extended dwell times between morning and evening trips. Weekday trip distances average 25 miles, while weekend trips average 42 miles. Charging simulations indicate over 90% of the U.S. school bus fleet could be electrified using current technologies (300-mile range at 1.21 kWh/mile with 19.2-kW depot charging) without modifying existing operating patterns. Depot charging is a key enabler of school bus electrification, however, the strategic placement of charging stations at other locations (e.g., schools) can further increase electrification potential. Additionally, we find electric school bus charging to be highly flexible, with charge management capable of reducing peak charging loads at depots by up to 77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Anisotropy, frustration, and saddle point in the twisted kagome compound ErPdPb

The kagome lattice, with its inherent geometric frustration, provides a rich platform for exploring intriguing magnetic phenomena and topological electronic structures. In reduced-symmetry structures, such as twisted kagome systems involving rare earth elements, additional anisotropy can arise, enabling intriguing properties including spin-ice states, magnetocaloric effects, noncollinear magnetic ordering, and the anomalous Hall effect. Here, we report the synthesis of single crystals of ErPdPb, which features a twisted kagome lattice net of Er atoms within the hexagonal ZrNiAl-type structure, and we investigate its magnetic, electronic, and thermal properties. The material exhibits a highly anisotropic, correlated magnetic state below 2.7 K, as evidenced by magnetic, transport, and heat capacity measurements. Density functional theory (DFT) calculations indicate strong easy-axis anisotropy, consistent with experiment and crystal-field expectations, as well as quasi-one-dimensional bands and a spin-split saddle point at the zone center. Here, the coexistence of competing magnetic interactions along different crystallographic directions suggests an inherent degree of frustration. ErPdPb thus provides a promising platform for exploring the interplay of frustration, anisotropy, and electronic structure in a twisted kagome lattice.

Antiferromagnets↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin (Final Technical Report)

This report documents the results of "A Play-Based Exploration of CO₂ Storage in the Illinois Basin" (DE-FE0032366), a project funded by the U.S. Department of Energy Office of Fossil Energy and Carbon Management and conducted by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in partnership with Visage Energy. The project adapted play-based exploration (PBE), a systematic basin-scale evaluation methodology from the petroleum industry, to screen areas of Illinois for commercial geologic carbon storage (GCS) in Cambro-Ordovician strata. The traditional play concept was expanded to encompass three play element groups, subsurface geologic factors, surface features, and societal factors, yielding 24 play elements with defensible suitability criteria applied through a five-tier classification scheme. An integrated geospatial database was assembled from ISGS, MGSC, MRCI, NATCARB, and public data sources, supported by significant data-improvement work including correction of legacy well locations, digitization of more than 1,300 well construction records using the DOE CATALOG team's OGRRE tool, compilation of a statewide 2D seismic database, and production of a refined fault and fold geodatabase.

58 GEOSCIENCES↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗
DOE OSTI2021

Determining the Three-Dimensional Structures of Silica-Supported Metal Complexes from the Ground Up

The immobilization of molecularly precise metal complexes to substrates, such as silica, provides an attractive platform for the design of active sites in heterogeneous catalysts. Specific steric and electronic variations of the ligand environment enable the development of structure–activity relationships and the knowledge-driven design of catalysts. At present, however, the three-dimensional environment of the precatalyst, much less the active site, is generally not known for heterogeneous single-site catalysts. We explored the degree to which NMR-based surface-to-complex interatomic distances could be used to solve the three-dimensional structures of three silica-supported metal complexes. Furthermore, the structure solution revealed unexpected features related to the environment around the metal that would be difficult to discern otherwise. This approach appears to be highly robust and, due to its simplicity, is readily applied to most single-site catalysts with little extra effort.

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

Simulating Pyrocumulonimbus Clouds Using a Multiscale Wildfire Simulation Framework

Pyrocumulonimbus (pyroCb) clouds, driven by extreme fires under favorable meteorological conditions, can inject smoke into the stratosphere at magnitudes comparable to those of moderate volcanic eruptions, potentially altering the global radiative balance and atmospheric composition. However, simulating pyroCb is particularly challenging in Earth system models. Using the Energy Exascale Earth System Model (E3SM), we developed a novel global multiscale framework to model pyroCb events in California, which includes a high‐resolution fire radiative power time series, a one‐dimensional plume‐rise parameterization, a fire‐induced vertical water vapor transport scheme, and a surface wildfire sensible heat flux representation. Our simulation successfully reproduces many pyroCb features, including cloud height, spatiotemporal evolution, and convective intensity in comparison with satellite and ground‐based observations. Sensitivity experiments show that realistic pyroCb simulation depends on vertical water vapor transport. These advances provide a basis for future exploration of pyroCb impacts at regional and global scales within climate models.

E3SM↗
DOE OSTI2024

Modern nuclear and astrophysical constraints of dense matter in a redefined chiral approach

Here, we explore the quantum chromodynamics (QCD) phase diagram’s complexities, including quark deconfinement transitions, liquid-gas phase changes, and critical points by using the chiral mean-field (CMF) model that is able to capture all these features. We introduce a vector meson field redefinition within the CMF framework, enabling precise adjustments of meson masses and coupling strengths related to vector meson interactions. Performing a new fit to the deconfinement potential, we are able to replicate recent lattice QCD results, low-energy nuclear physics properties, neutron star observational data, and key phase diagram features as per modern constraints. This approach enhances our understanding of vector mesons’ roles in mediating nuclear interactions and their impact on the equation of state, contributing to a more comprehensive understanding of the QCD phase diagram and its implications for nuclear and astrophysical phenomena.

QCD phase transitions↗
DOE OSTI2024

NuclPred v1

This tool takes a genome assembly as input and predicts per-site nucleosome occupancy as output. Trained on physical maps of nucleosome binding preferences across the fungal kingdom, NuclPred can be applied broadly across fungi (and other eukaryotes). This breadth, combined with its accuracy, means it could have both basic and applied biological implications, for example in understanding eukaryotic gene regulation and genetic engineering. Almost universally across eukaryotes, nucleosomes - each wrapping ~150 base pairs of DNA - serve to package DNA inside the nucleus, with major consequences on DNA access, gene activity and DNA integration. NuclPred was generated using a supervised deep learning approach combining convolutional and recurrent neural networks to take DNA features (nucleotides, GC content and structural information) as input, then use that information to predict the physical attractiveness DNA sequences might have for forming nucleosomes. With this information at hand, researchers can design more efficient CRISPR constructs, explore the interplay between DNA signatures and other regulators impact nucleosome locations, predict expression patterns, etc. This tool will be published as part of a manuscript currently under revision at iScience (draft attached).

Mondo, Stephen↗
DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗
DOE OSTI2024

Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation

Monitoring the status of a high throughput computing cluster running computationally intensive production jobs is a crucial yet challenging system administration task due to the complexity of such systems. To this end, we train autoencoders using the Linux kernel CPU metrics of the cluster. Additionally, we explore assisting these models with graph neural networks to share information across threads within a compute node. The models are compared in terms of their ability to: 1) Produce a compressed latent representation that captures the salient features of the input, 2) Detect anomalous activity, and 3) Make distinction between different kinds of jobs run at Jefferson Lab. The goal is to have a robust encoder whose compressed embeddings are used for several downstream tasks. We extend this study further by deploying these models in a human-in-the-loop production-based setting for the anomaly detection task and discuss the associated implementation aspects such as continual learning and the criterion to generate alarms. This study represents a first step in the endeavor towards building self-supervised large-scale foundation models for computing centers.

Mohammed, Ahmed↗