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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Characterization of the microstructure of yttrium hydride under proton irradiation

High moderation per unit volume solid moderator materials like yttrium hydride (YH x ) are necessary for compact nuclear microreactors. However, the phase stability and hydrogen transport processes of YH x under high-temperature irradiation are largely unknown. Proton irradiation was conducted on YH x at 300 °C and 580 °C to 0.2 dpa using 1 MeV or 2 MeV protons in a high-vacuum environment. The hydrogen concentration was determined before and after irradiation using elastic recoil detection analysis, and microstructural evolution was examined via post-irradiation scanning transmission electron microscopy and Raman spectroscopy. Dislocation loops and cavities were observed in all conditions; their distribution was correlated with the bombarding proton energy and ion irradiation temperature. This work revealed that hydrogen retention is proportional to the formation of traps for hydrogen gas atoms and identified pathways for hydrogen release. The relative contributions of bulk or fast diffusion paths, such as grain boundaries, delamination boundaries, and stacking faults are discussed; the primary mechanisms of hydrogen loss are likely based on diffusion, ruling out artefacts of the experimental design. In conclusion, the study suggests proton irradiation may be a strong surrogate to study hydrogen transport in hydride moderator materials under irradiation.

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

Molecular-dynamics study of diffusional creep in uranium mononitride

Uranium mononitride (UN) is a promising advanced nuclear fuel due to its high thermal conductivity and high fissile density. Yet, many aspects of its mechanical behavior and microstructural features are currently unknown. In this paper, molecular dynamics (MD) simulations are used to study UN's diffusional creep. Nanometer-sized polycrystals are used to simulate diffusional creep and to calculate an effective GB width. It is found that Nabarro-Herring creep is not dominant in the temperature range of 1700–2000 K and that the dominant diffusional creep mechanism is Coble creep with an activation energy of 2.28 ± 0.09 eV. A method is proposed to calculate the diffusional GB width and its temperature dependence in polycrystals. The effective GB width of UN is calculated as 2.69 ± 0.08 nm. This value fits very well with the prefactor of the phenomenological Coble creep formula. It is demonstrated that the most comprehensive thermal creep model for UN can be represented as the combination of our Coble creep model and the dislocation creep model proposed by Hayes et al.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Analytical workflow dependence of experimental observables for uranium chemistries

In this work, we investigated how the sequencing of laboratory analytical methods used for chemical and morphological characterization influences analytical findings for particulate materials relevant to the nuclear fuel cycle, including UO2, U3O8, studtite (UO2O2·4H2O), and β-UO3, in the context of nuclear forensic analysis. Particles of each chemistry obtained from consistent production batches were exposed to Raman spectroscopy and scanning electron microscopy in varying orders to elucidate how the order in which the techniques are applied influences morphological and chemical observations as a function of particle size. The results indicate that particles from all four chemistries exposed to high-resolution electron imaging before Raman spectral analysis demonstrate optical vibrational spectral changes that reduce accurate interpretation of the underlying chemistry via Raman spectral analysis. We hypothesize that these changes are due to the thermal load of the electron beam imparted to the sample being unable to be dissipated by materials with poor thermal conduction properties. Results from this study will aid in determining best practices for forensic analysis procedures to reduce uncertainty in chemical determination of unknown particulate samples.

Manns, Rebecca [University of Nevada, Las Vegas]↗
DOE OSTI2025

Dynamics of disordered intermediates during the two-electron alkaline MnO 2 conversion reaction for grid-scale batteries

Battery technologies beyond Li-ion are likely needed for extensive integration of grid-scale storage. The rechargeable Zn-MnO 2 chemistry has the potential for high sustainability, high safety, and low cost, using Earth-abundant basis materials. In an alkaline electrolyte, the MnO 2 cathode can cycle reversibly if modified by including a Bi-containing additive, although the cycling mechanism remains mostly unknown. This work presents an account of the intermediate species involved in the electrochemical transformation from layered δ-MnO 2 to Mn(OH) 2 and back. During charge, a disordered intermediate with a structure resembling layered β-MnOOH exists stably for an extended period, corresponding to a regime known to have unexpected electrochemical activity of Bi. During discharge, β-MnOOH exists only briefly and is never the majority material, revealing that the cycling mechanism is asymmetric. In conclusion, these findings represent a significant advance in mechanistic knowledge and can enable engineering to develop the system for commercial use.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Consequences of long-term water exposure for bulk crystal structure and surface composition/chemistry of nickel-rich layered oxide materials for Li-ion batteries

Water exposure of layered nickel-rich transition metal oxide electrodes, widely used in high-energy lithium-ion batteries, has detrimental effects on the electrochemical performance, which complicates electrode handling and prevents implementation of environmentally benign aqueous processing procedures. Elucidating the degradation mechanisms in play may help rationally mitigate/circumvent key challenges. Here, the bulk structural consequences of long-term (>2.5 years) deuterated water (D2O) exposure of intercalation materials with compositions Li x Ni 0.5 Co 0.2 Mn 0.3 O 2 (NCM523) and Li x Ni 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) are studied by neutron powder diffraction (NPD). Detailed inspection of the NPD data reveals gradual formation of a secondary crystalline phase in all exposed samples, not previously reported for this system. This unknown phase forms faster in liquid- compared to vapor-exposed compounds. Structural modelling of the NPD data shows a stable level of Li/Ni anti-site defects and does not indicate any significant changes in lattice parameters or hydrogen-lithium (D + /Li + ) exchange in the structure. Consequently, the secondary phase formation must take place via transformation rather than modification of the parent material. X-ray photoelectron spectroscopy data indicate formation of LiHCO 3 /Li 2 CO 3 at the surface and a Li-deficient oxide in the sub-surface region of the pristine compounds, and the presence of adsorbed water and transition metal hydroxides at the exposed sample surfaces.

25 ENERGY STORAGE↗
DOE OSTI2022

Electric vehicle post-crash Recovery—Stranded energy issues and mitigation strategy

In post-crash situations, passengers, bystanders, and first responders are exposed to the immediate safety risks of stranded energy in electric vehicle (EV) batteries. Stranded energy is the energy remaining inside any undamaged or damaged battery following an accident. A potentially damaged battery with an unknown state of safety might go into thermal runaway in the absence of proper monitoring, diagnostics, controls, and handling—thereby leading to potential loss of life and property damage. Therefore, it is imperative to develop standard guidelines, diagnostic methods, and tools to handle stranded energy appropriately and minimize safety risks from immediately after an EV accident to final disposal or re-entry to the road. This perspective paper identifies the challenges associated with handling stranded energy and proposes some potential solution pathways. Further, we hope to draw attention of the research and development community, original equipment manufacturers, and policymakers for motivating new insights and research needs in mitigating safety implications associated with stranded energy.

25 ENERGY STORAGE↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2019

The total microbiome functions in bacteria and fungi

Unveiling the relationship between phylogeny and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial phylogenetic diversity, our understanding of its relationship to functional diversity is still scarce. Here we predicted the total microbiome functions of bacteria and fungi on Earth using the total known functions from level 3 of KEGG Orthology by modelling the increase of functions with increasing diversity of bacteria or fungi. For bacteria and fungi, the unsaturated model described the data significantly better (for both P?<2.2e-16), suggesting the presence of two types of functions. Widespread functions ubiquitous in every living organism that make up two thirds of our current knowledge of microbiome functions are separated from rare functions from specialised enzymes present in only a few species. Given previous estimates on species richness, we predicted a global total of 35.5 million functions in bacteria and 3.2 million in fungi; of which only 0.02% and 0.14% are known today. Our approach highlights the necessity of novel and more sophisticated methods to unveil the entirety of rare functions to fully understand the involvement of the microbiome in ecosystem functioning. SIGNIFICANCE: The functionality of and within a microbial community is generally inferred based on the taxonomic annotation of the organism. However, our understanding of functional diversity and how it relates to taxonomy is still limited. Here we predict the total microbiome functionality in bacteria and fungi on Earth using known and annotated protein-coding sequences in species accumulation curves. Our estimates reveal that the majority of functionality (>99%) could be assigned to yet unknown and rare functions, highlighting that our current knowledge is incomplete and functional inference is thus lackluster.

Bacteria↗
DOE OSTI2022

Structure of maize BZR1-type β-amylase BAM8 provides new insights into its noncatalytic adaptation

Plant β-amylase (BAM) proteins play an essential role in growth, development, stress response, and hormone regulation. Despite their typical (β/α) 8 barrel structure as active catalysts in starch breakdown, catalytically inactive BAMs are implicated in diverse yet elusive functions in plants. Further, the noncatalytic BAM7/8 contain N-terminal BZR1 domains and were shown to be involved in the regulation of brassinosteroid signaling and possibly serve as sensors of yet an uncharacterized metabolic signal. While the structures of several catalytically active BAMs have been reported, structural characterization of the catalytically inactive BZR1-type BAMs remain unknown. Here, we determine the crystal structure of β-amylase domain of Zea mays BAM8/BES1/BZR1-5 and provide comprehensive insights into its noncatalytic adaptation. Using structural-guided comparison combined with biochemical analysis and molecular dynamics simulations, we revealed conformational changes in multiple distinct highly conserved regions resulting in rearrangement of the binding pocket. Altogether, this study adds a new layer of understanding to starch breakdown mechanism and elucidates the acquired adjustments of noncatalytic BZR1-type BAMs as putative regulatory domains and/or metabolic sensors in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Microstructure and CPO evolution of dynamically recrystallized olivine during complex deformation conditions: a full-field numerical modeling approach

The rheological properties of mantle rocks are strongly dependent on their crystallographic preferred orientation (CPO). Olivine CPO, defined by the orientation of seismically fast [100] axes parallel to flow direction, is also thought to be a dominant contributor to seismic anisotropy in the Earth's upper mantle. However, the amount of deformation needed to overprint a new CPO on a pre-existing fabric and the impact of the inherited CPOs on the transient microstructure evolution, remain unknown. This study employs a full-field numerical approach (VPFFT-ELLE) to explore the dynamic recrystallization and microstructural evolution of olivine polycrystalline aggregates under complex deformation conditions. We test four combinations of successive pure shear and simple shear boundary conditions. Findings indicate that inherited CPOs influence subsequent deformation in a manner dependent on the kinematic relationship between successive stages. In all cases, a minor strain increment (ε ∼0.3–0.6) is sufficient to erase the previous microstructure and CPO. However, when deformation conditions change dramatically (e.g., stretching direction changes orthogonally), the intensity of the new CPO developed is significantly lower and strain distribution are specially altered. During a transient strain stage, pre-existing microstructures undergo extensive reworking, especially when deformation conditions are changed dramatically, such as switching from simple shear to a pure shear condition with a parallel shortening direction relative to the stretching direction. We estimate the significance of these results in interpreting observations of seismic velocity anisotropy, concluding that P-wave seismic anisotropy is significantly and positively correlated with the evolution of olivine CPO with deformation history. This research underscores the transient nature of microstructural rearrangement in olivine aggregates and the necessity for caution in interpreting seismic anisotropy in regions with complex deformation histories, as inherited CPOs can influence current fabric development and induce deviation to the present deformation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating rapid alpha-decay induced aging of 238 PuO 2 by Raman Spectroscopy

Investigating alpha-decay induced aging in PuO 2 is useful in nuclear forensics; helping to determine the time since last calcination. This was previously investigate by monitoring the damage to 240 PuO 2 over the course of a few years. The rate of alpha-decay induced aging has long been assumed to scale only with the decay rate of other isotopes without direct verification. This article reports the first alpha-decay aging study of 238 PuO 2 by Raman spectroscopy. Contrary to the expected 10 days for 238 PuO 2 to reach steady state based on 240 PuO 2 studies, the alpha aging curve reached an approximate steady state ∼24–30 h after laser annealing. While the cause of the order of magnitude decrease is unknown, it is speculated that dynamic annealing could contribute to the differences in rate of induced aging. The Raman spectra of annealed and aged 238 PuO 2 matched the expected features in 239 PuO 2 and 240 PuO 2 spectra; showing no formation of new stable chemical species in the material such as secondary Pu oxide phases (e.g., Pu 4 O 9 ). In conclusion, results indicate that 238 Pu could be leveraged for rapid alpha-decay aging studies to characterize alpha-decay induced features and better understand matrix temperatures and the annealing of Frankel pair defects.

Actinide↗
DOE OSTI2023

Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset

The first- and last-mile (FM/LM) problem is a major deterrent to public transit use. With the rise of shared micromobility options such as shared e-scooters in recent years, there is a growing interest in understanding their potential to serve as a last-mile transit solution. However, empirical data regarding the integrated use of shared micromobility and public transit have been limited so far. As a result, much is unknown regarding the spatiotemporal patterns and characteristics of shared micromobility trips serving as an FM/LM connection to transit. Here, this paper addresses these knowledge gaps by leveraging a novel dataset (i.e., the Spin post-ride survey dataset) that records thousands of transit-connecting shared e-scooter trips in Washington DC. Specifically, we used the dataset to reveal the spatiotemporal patterns of transit-connecting shared e-scooter trips in Washington DC, resulting in some major policy insights regarding the integral use of shared e-scooters and public transit. We further leveraged the dataset to validate if and to what extent a commonly applied buffer-zone approach can infer FM/LM micromobility trips accurately. Statistical tests showed that the actual FM/LM Spin e-scooter trips differ from inferred FM/LM Spin e-scooter trips in both spatial and temporal dimensions. This indicates that the common practice of inferring FM/LM micromobility trips with a buffer-zone approach can lead to inaccurate estimates of transit-connecting micromobility trips.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Nanoindentation mapping defects filtration for heterogeneous materials using generative adversarial networks

Advanced composite materials with multiple phases and heterogeneous microstructure necessitate spatial mapping characterization of elastic modulus to develop constitutive relations and overall mechanical response. Such modulus mapping can be obtained using the nanoindentation technique, where the indenter tip raster over the selected microstructure region. Typically, a surface preparation procedure is done in the specimens to ensure proper contact between the indenter tip and sample surface. However, a near-perfect surface finish is unachievable in heterogeneous materials, primarily with ceramic reinforcements, due to the differential material removal rate during polishing. Thus, the nanoindenter records localized erroneous measurements due to differences in surface roughness and corresponding force response. This study establishes a novel deep learning-based strategy to rectify incorrect experimental spatial measurements acquire during nanoindentation modulus mapping. Here, the integrated bicubic interpolation and generative adversarial networks (GANs) model was trained using 14 ceramic and 18 metallic data sets, each comprising 65,536 measurements. The developed algorithm was validated against experimental measurements on four unknown specimens. The standard deviation in measured elastic modulus reduces by ~50% in ceramics and ~72% in metallic samples. This computational framework proposes a novel approach to reducing uncertainty in materials’ properties using state-of-the-art computer vision techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A database of ultrastable MOFs reassembled from stable fragments with machine learning models

High-throughput screening of hypothetical metal-organic framework (MOF) databases can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures with orders of magnitude more (1) connectivity nets and (2) inorganic building blocks than were present in prior databases. Further, this database shows a 10-fold enrichment of ultrastable MOF structures that are stable upon activation and more than 1 standard deviation more thermally stable than the average experimentally characterized MOF. For nearly 10,000 ultrastable MOFs, we compute elastic moduli to confirm that these materials have good mechanical stability, and we report methane deliverable capacities. We identify privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH 3 ). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computational Modeling and Evolutionary Implications of Biochemical Reactions in Bacterial Microcompartments

Bacterial microcompartments (BMCs) are protein-encapsulated compartments found across at least 23 bacterial phyla. BMCs contain a variety of metabolic processes that share the commonality of toxic or volatile intermediates, oxygen-sensitive enzymes and cofactors, or increased substrate concentration for magnified reaction rates. These compartmentalized reactions have been computationally modeled to explore the encapsulated dynamics, ask evolutionary-based questions, and develop a more systematic understanding required for the engineering of novel BMCs. Many crucial aspects of these systems remain unknown or unmeasured, such as substrate permeabilities across the protein shell, feasibility of pH gradients, and transport rates of associated substrates into the cell. This review explores existing BMC models, dominated in the literature by cyanobacterial carboxysomes, and highlights potentially important areas for exploration.

bacterial microcompartments↗
DOE OSTI2025

Ecology of methyl-coenzyme M reductase encoding Thermoproteota

The recent demonstration that members of at least three classes of archaea affiliated with the Thermoproteota superphylum are involved in the production of the climate-active gas methane has sparked discussions about how well we understand the diversity of methanogens. Here, in this work, we show that members of all three of these lineages, as well as several other, yet uncultured and physiologically uncharacterized groups within the Thermoproteota that encode the key enzyme of anaerobic methane cycling, methyl-coenzyme M reductase (MCR), are widely distributed in anoxic ecosystems. We postulate that the taxonomic, metabolic, and ecological diversity of methanogenic and MCR-encoding Thermoproteota are poorly understood, and that the contribution of methylotrophic and thermoproteotal methanogenesis to methane production is largely unknown. We hypothesize that thermoproteotal methanogens could contribute, potentially substantially, to methane emissions in many anoxic environments that harbor methylated precursors, including wetlands, sediments, peat, rice paddies, wastewater sludge, and geothermal systems. We highlight the necessity to experimentally test the (eco)physiology of these widely distributed archaea using both culture-dependent ( in vitro ) and culture-independent ( in situ ) approaches to assess their potential contribution to methane emissions. Last, we stress the importance of remaining agnostic about the physiology of MCR-encoding Thermoproteota in the absence of experimental data because most of these archaea also carry the genetic potential to grow non-methanogenically.

MCR↗
DOE OSTI2021

Structural and mechanistic basis for protein glutamylation by the kinase fold

The kinase domain transfers phosphate from ATP to substrates. However, the Legionella effector SidJ adopts a kinase fold, yet catalyzes calmodulin (CaM)-dependent glutamylation to inactivate the SidE ubiquitin ligases. The structural and mechanistic basis in which the kinase domain catalyzes protein glutamylation is unknown. Here, in this work, we present cryo-EM reconstructions of SidJ:CaM:SidE reaction intermediate complexes. We show that the kinase-like active site of SidJ adenylates an active-site Glu in SidE, resulting in the formation of a stable reaction intermediate complex. An insertion in the catalytic loop of the kinase domain positions the donor Glu near the acyl-adenylate for peptide bond formation. Our structural analysis led us to discover that the SidJ paralog SdjA is a glutamylase that differentially regulates the SidE ligases during Legionella infection. Our results uncover the structural and mechanistic basis in which the kinase fold catalyzes non-ribosomal amino acid ligations and reveal an unappreciated level of SidE-family regulation.

59 BASIC BIOLOGICAL SCIENCES↗