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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Criticality Accidents in Experiments and Reactors [Slides]

The objectives of this presentation are to: (1) gain awareness of the historical timelines and trends associated with the occurrences of critical experiment accidents associated, (2) identify lessons learned from accidents with critical experiments and the development of standards for handling nuclear material and performance of critical experiments; and (3) to ensure trainees gain a working knowledge of the factors that lead to the accidents.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Rational Selection of Transition-Metal Oxide Electrocatalysts from Structure Electronic Structure-Activity Relations: The Role of Defects, Strain, and Sub-Surface Layering

This BES research investigates the physicochemical properties of metal oxides and how they affect electrocatalytic functionalities. The goal is to develop a predictive framework for realizing top-performing electrocatalytic materials for energy-critical reactions. The hypothesis is that well-defined thin films allow detailed mapping of structural-activity relationships because flat surfaces are more straightforward to characterize. Furthermore, the well-defined nature of thin-film metal oxides allows precise tuning of structural and chemical variables for structure-activity-relationship mapping. The proposed research has two technical objectives. The first is to experimentally assess whether the binding energies of surface oxygen can serve as an activity descriptor for oxygen electrocatalysis on metal oxides and then how to control them by tuning the structure and chemical variables. The second is to find the rate-limiting process in oxygen electrocatalysis and other small-molecule reactions. Similar mechanistic insights have been developed on well-defined platinum surfaces but not yet on oxides. This research addresses this gap and uses electrochemistry and X-ray photoemission spectroscopy to study oxide surface chemistry. The obtained insights are collectively analyzed to reveal how the oxides’ structural and chemical variables affect kinetics and can be used to design more active electrocatalysts for energy-critical reactions.

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

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Material Control & Accounting Modeling Developments for a Generic TRISO Fuel Fabrication Facility

The Material Protection, Accounting, and Control Technologies (MPACT) program utilizes modeling and simulation to assess Material Control and Accountability (MC&A) concerns for a variety of nuclear facilities. The Sandia National Laboratories (SNL)-developed Fissile Facility Flow Modeler (F3M) and the Material Accountancy Performance Indicator Toolkit (MAPIT) have historically provided MPACT with the capability to analyze MC&A approaches for nuclear facilities to determine that these facilities meet regulatory requirements. In FY25, improvements on the application of the F3M and MAPIT tools to simulate a generic TRi-structural ISOtropic (TRISO) fuel fabrication facility were successfully completed. The generic TRISO fuel fabrication F3M model captures the entire TRISO fuel fabrication process and is adaptable to any final TRISO fuel form, including spherical pebbles and cylindrical compacts loaded into graphite prismatic blocks. Comprehensive F3M/MAPIT functionality for the generic TRISO fuel fabrication model has been demonstrated. This modeling framework can be applied to support the U.S. Department of Energy and domestic nuclear industry stakeholders in developing MC&A approaches for advanced fuel fabrication facilities via statistical tests that demonstrate compliance to regulatory requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Reducing the Cost of Fatigue Crack Growth Testing for Storage Vessel Steels in Hydrogen Gas

Hydrogen storage pressure vessels are designed against fatigue crack growth, and the ASME Boiler and Pressure Vessel Code requires the fatigue crack growth rate (da/dN) vs. stress-intensity factor range (ΔK) relationship of the construction steel to be measured directly in hydrogen gas. These measurements are notoriously slow and expensive: the cyclic loading frequencies prescribed by standards (often 0.1 Hz) are two or more orders of magnitude below those for conventional fatigue testing, individual tests run for days to weeks, and the high-pressure test-chamber set-up imposes a significant per-specimen labor cost. Due to these time and cost constraints, near-threshold data—the regime most valuable for extending design fatigue life—are rarely generated for ferritic storage vessel steels in hydrogen gas.

08 HYDROGEN↗
DOE OSTI2020

Exoproteomics Reveals Outer Membrane Vesicle Mediated Catabolism of Lignin-Derived Aromatics by P. putida KT2440

Lignin is an abundant component of plant cell walls which is heterogeneous and highly resistant to degradation. Biological funneling of lignin via microbes represents an efficient approach for reducing chemical heterogeneity and generating molecules which can be valorized into performance-advantaged materials. Pseudomonas putida KT2440 (P. putida) catabolizes lignin-derived aromatic monomers rapidly and efficiently via the b-ketoadipate (bKA) pathway and our data suggest P. putida may depolymerize HMW lignin as well [1]. The mechanism by which HMW lignin is depolymerized by bacteria remains unknown. Intriguingly, we found that P. putida secretes outer membrane vesicles (OMVs) of two distinct sizes in the presence of lignin. Here, we describe the temporal proteome of OMVs and the vesicle-free secretome (VFS) in the presence or absence of lignin. Enzymes involved in aromatic catabolism via the bKA pathway are temporally enriched into OMVs in the presence of lignin. Isolated OMVs enhance growth of P. putida mutants unable to grow on model lignin-derived aromatics, and turnover protocatechuic acid in vitro. Together, this work leads us to hypothesize mechanisms by which OMVs contribute to extracellular aromatic catabolism by P. putida. Ongoing and future work seeks to further understand this phenomena in the context of both fundamental an applied lignin conversion research areas.

09 BIOMASS FUELS↗
DOE OSTI2020

Growth and Properties of Vapor Diffused Nb3Sn Coating for Superconducting Radiofrequency Accelerator Cavity Applications

The desire to reduce construction and operating costs of future SRF accelerators motivates the search for higher-performing alternative materials. Nb3Sn (Tc ~ 18.3 K and Hsh ~ 425 mT) is the front runner. The tin vapor diffusion process is currently the technique of choice to produce promising Nb3Sn-coated cavities. Understanding Nb3Sn nucleation and growth in this process is essential to progress. Samples representing different stages of Nb3Sn formation have been produced and studied to elucidate the effects of nucleation, growth, process conditions, and impurities. Nb3Sn films with thickness from a few hundred nm up to ~15 µm were grown and characterized. The microscopic examination of samples suggests the mechanisms of thin film nucleation and growth. Broadly, nucleation deposits tin as a thin surface phase and, under some conditions, a few hundred nanometer sized particles as well. Conditions that impair nucleation promote the formation of defects, such as ?patches?, in subsequent coating growth. Analysis of coated samples is consistent with the model of Nb3Sn grown in which tin diffuses via grain boundaries to Nb3Sn-Nb interface, where the growth Nb3Sn into the niobium bulk takes place. Similar scaling laws are found for grain growth and layer thickness. Non-parabolic layer growth is consistent with significant grain growth, which reduces the number of Sn-transport channels. Examination of patchy region in Nb3Sn coating revealed it to be large single crystalline grains, pointing to impeded Nb3Sn layer growth due to low grain boundary density, resulting in a significantly thin coating in those areas. Examination of RF loss regions from a coated cavity, identified with a thermometry mapping system showed patchy regions and carbonus defects were associated with strong local field-dependent surface resistance. RF measurements of coated cavities were combined with material characterization of witness samples and coated-cavity cutouts to improve the coating process. Understanding obtained and applied to cavity coatings, resulted in single-cell Nb3Sn cavities with a quality factor of ~2 ×1010 up to 15 MV/m accelerating gradient at 4 K, without "Wuppertal" Q-slope. We have also produced Nb3Sn-coated CEBAF 5-cell cavities with accelerating gradients useful for accelerator cryomodules. This dissertation will discuss the genesis of the Nb3Sn coating in a typical tin vapor diffusion process, effects of different process parameters, and its consequences to the coating of single-cell and multi-cell SRF cavities.

Pudasaini, Uttar↗
DOE OSTI2021

Anisotropic inelastic materials milestone 2021

The focus on new fuel and cladding models are drivers for several new capabilities at the MOOSE and BISON levels. Since these materials are anisotropic, they require an extension of our inelastic mechanics capabilities, which currently heavily rely on radial return mapping and the assumption of isotropic stiffness. We plan to add additional code paths to support fully anisotropic materials, without degrading the performance for modeling isotropic materials.

42 ENGINEERING↗
DOE OSTI2021

“Flower-like” Li 4 Ti 5 O 12 -multiwalled carbon nanotube composite structures with performance as highrate anode-materials for li-ion battery applications and methods of synthesis thereof

A method of fabricating nanocomposite anode material embodying a lithium titanate (LTO)-multi-walled carbon nanotube (MWNT) composite intended for use in a lithium-ion battery includes providing multi-walled carbon nanotube (MWNTs), including nanotube surfaces, onto which functional oxygenated carboxylic acid moieties are arranged, generating 3D flower-like, lithium titanate (LTO) microspheres, including thin nanosheets and anchoring the acid-functionalized MWNTs onto surfaces of the 3D LTO microspheres by π-π interaction strategy to realize the nanocomposite anode material.

25 ENERGY STORAGE↗
DOE OSTI2022

Magnetite (Fe 3 O 4 )—multiwalled carbon nanotube composite structures with performance as high rate electrode materials for Li-ion batteries

A method of synthesizing an electrode material for lithium ion batteries from Fe 3 O 4 nanoparticles and multiwalled carbon nanotubes (MWNTs) to yield (Fe 3 O 4 -NWNTs) composite heterostructures. The method includes linking the Fe 3 O 4 nanoparticles and multiwalled carbon nanotubes using a π-π interaction synthesis process to yield the composite heterostructure electrode material. Since Fe 3 O 4 has an intermediate voltage, it can be considered an anode (when paired with a higher voltage material) or a cathode (when paired with a lower voltage material).

Wong, Stanislaus↗
DOE OSTI2022

Methods and systems for treatment of superconducting materials to improve low field performance

A system and method for treating a cavity comprises preparing a superconducting radio frequency (SRF) cavity for removal of a dielectric layer from on an inner surface of the SRF cavity, subjecting the SRF cavity to a heat treatment in order to remove the dielectric layer from the inner surface of the SRF cavity, and preventing the development of a new dielectric layer on the inner surface of the SRF cavity by preventing an interaction between the inner surface of the SRF cavity and atmospheric gasses.

Romanenko, Alexander↗
DOE OSTI2021

Application of the FaMUS Methodology to the NSUF Research Outputs Through End of 2019

The Nuclear Science User Facilities (NSUF) is one of a diverse group of DOE user facilities. It is focused on advancing the understanding of radiation effects in nuclear fuels and materials in support of nuclear energy applications. The NSUF has been operating since 2007 and has developed a significant portfolio of supported research. Therefore, it is appropriate to consider its achievements and to determine its successes and shortfalls. As part of this analysis of the NSUF research program, the NSUF has developed a novel and elegant formalism for assessing the current level of understanding of nuclear fuels and materials for use in nuclear environments: the NSUF Fuels and Materials Understanding Scale (FaMUS). The FaMUS methodology is being applied to the NSUF portfolio on an ongoing basis to quantify the progress made. This report summarizes the status of the assessment exercise and highlights general lessons learned. This examination will facilitate capability balancing of future research by identifying gaps in knowledge, understanding, testing, and demonstration allowing the NSUF program office in conjunction with DOE-NE to direct resources to important aspects of the DOE-NE mission using “emphasize & enhance”, “maintain”, and “encourage excellence” classifications as well as providing increased transparency to researchers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗