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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Reconstructing Oxygen-Deficient Zirconia with Ruthenium Catalyst on Atomic-Scale Interfaces toward Hydrogen Production

Downsizing a catalyst nanoparticle (NP) to a single atom (SA) has proven to be highly effective in increasing catalytic activity and decreasing the amount of catalyst required for various electrochemical reactions. However, insufficient stability of the single-atom site catalysts (SACs) is still a significant challenge for their practical application. Here, SACs firmly bound to stable metal oxide NPs are proposed to dramatically increase the electrochemical activity and stability of SA-based catalysts for hydrogen evolution reaction (HER). Starting from a Ru-infiltrated, Zr-based metal-organic framework (MOF), the tetragonal zirconium oxide (ZrO 2-x ) NPs-embedded carbon matrix is fabricated as support through facile pyrolysis. Simultaneously, Ru SAs as active sites are well dispersed on the surface of ZrO 2-x NPs due to the generation of oxygen vacancies in the tetragonal ZrO 2-x . Finally, the Ru-ZrO 2-x SAC exhibits a 4–5 times higher mass activity than commercial Pt and Ru catalysts and superior durability due to strong metal-support interaction (SMSI) between Ru atoms and ZrO 2-x substrate.

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

Key Elements of Auroral Substorm Development and their Relationship to Recent Observations of Detached Sub-Auroral Phenomena Including STEVE-like Emissions

Since Akasofu introduced the concept of the substorm in 1964, numerous refinements have been added to the phenomenological model of how auroral substorms develop. Here, we review several of the most important of these including; polar cap patches, intensifications of the Polar Activated Band (PAB) at the poleward edge of the bulge, the development of new arcs poleward, the generation of auroral streamers, the evolution of streamers into auroral torches and omega bands, the association of streamers with particle injection and BBF activity in the tail, and wedgelet-like magnetic perturbations on the ground. We also review the concept of “contact breakups” which are breakups that appear to be triggered by the arrival of streamer-like forms in the equatorward regions of the oval. Finally, we remind the community that the addition of detached sub-auroral emissions was made to the phenomenological picture of substorm develop in the late 1960s along with the concept of auroral streamers. Newer observations of detached sub-auroral (STEVE-like emissions) from the Viking/UVI and POLAR/VIS imagers are presented which confirm that they are east-west aligned bands of emission that separate away from the equatorward edge of the auroral oval in response to intensifications of the poleward edge of the bulge and subsequent streamer production. We propose a new model for the formation of detached sub-auroral (STEVE-like) emissions in which a non-linear growth of a SAPS-driven instability (e.g. like the shear-flow/ballooning instability) at the plasmapause results in the disruption of the boundary separating hot plasma sheet particle from the cold plasmaspheric plasma. It is proposed that the resulting intermixing of plasma populations leads to both the observed STEVE-like emissions and also provides a source of cold plasma on open drift paths that can feed the long-lived drainage plumes that have recently been discovered.

58 GEOSCIENCES↗
DOE OSTI2021

Replacing Pyridine with Pyrazine in Molecular Cobalt Catalysts: Effects on Electrochemical Properties and Aqueous H2 Generation

Four new molecular Co(II)tetrapyridyl complexes were synthesized and evaluated for their activity as catalysts for proton reduction in aqueous environments. The pyridine groups around the macrocycle were substituted for either one or two pyrazine groups. Single crystal X-ray analysis shows that the pyrazine groups have minimal impact on the Co(II)–N bond lengths and molecular geometry in general. X-band EPR spectroscopy confirms the Co(II) oxidation state and the electronic environment of the Co(II) center are only very slightly perturbed by the substitution of pyrazine groups around the macrocycle. The substitution of pyrazine groups has a substantial impact on the observed metal- and ligand-centered reduction potentials as well as the overall H2 catalytic activity in a multimolecular system using the [Ru(2,2′-bipyridine)3]Cl2 photosensitizer and ascorbic acid as a sacrificial electron donor. The results reveal interesting trends between the H2 catalytic activity for each catalyst and the driving force for electron transfer between either the reduced photosensitizer to catalyst step or the catalyst to proton reduction step. The work presented here showcases how even the difference of a single atom in a molecular catalyst can have an important impact on activity and suggests a pathway to optimize the photocatalytic activity and stability of molecular systems.

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

Coulomb Interaction-Driven Entanglement of Electrons on Helium

The generation and evolution of entanglement in many-body systems is an active area of research that spans multiple fields, from quantum information science to the simulation of quantum many-body systems encountered in condensed matter, subatomic physics, and quantum chemistry. Motivated by recent experiments exploring quantum information processing systems with electrons trapped above the surface of cryogenic noble gas substrates, we theoretically investigate the generation of entanglement between two electrons via their unscreened Coulomb interaction. The model system consists of two electrons confined in separate electrostatic traps that establish microwave-frequency quantized states of their motion. We compute the motional energy spectra of the electrons, as well as their entanglement, by diagonalizing the model Hamiltonian with respect to a single-particle Hartree product basis. We also compare our results with the predictions of an effective Hamiltonian. The computational procedure outlined here can be employed for device design and guidance of experimental implementations. In particular, the theoretical tools developed here can be used for fine-tuning and optimization of control parameters in future experiments with electrons trapped above the surface of superfluid helium or solid neon. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Unraveling the Cooperative Activity of Hydrophilicity, Conductivity, and Interfacial Active Sites in Alginate‐CNT‐Cuo Self‐Standing Electrodes with Benchmark‐Close Activity for Alkaline Water Splitting

Designing electrocatalysts that excel in hydrogen and oxygen electrochemistry is crucial for sustainable hydrogen generation through electrochemical water splitting. This study presents a novel tricomponent catalyst composed of an alginate hydrogel (AL) infused with single-walled carbon nanotubes (CNTs) and copper oxide (CuO) nanoparticles. The catalyst exhibits benchmark-close bifunctional activity toward hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) under alkaline conditions. The aerophobic nature of the AL-gel facilitates superior bubble release from the electrode, while the inclusion of CNTs mitigates charge transfer resistance. Moreover, heterojunctions of CuO and CNTs create unique interfacial active sites, culminating in high electrocatalytic water-splitting activity. The structural rigidity of the composite permits its use as self-standing electrodes (SSE) without using substrates or binders, enabling a direct evaluation of its activity. The composite electrode demonstrates exceptional electrocatalytic HER activity in an alkaline solution, with onset potentials of 93 mV and moderate OER activity with an onset of 155 mV. Furthermore, a water electrolysis cell featuring the bifunctional SSE exhibits an open circuit voltage of 1.85 V at 100 mA.cm −2 , and only 8% efficiency loss after 100 h marking this a significant stride in developing self-standing nonprecious electrocatalysts with impressive catalytic performance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Illumination of Thunderclouds by Lightning: 4. Volumetric Thunderstorm Imagery

Optical instruments such as the Geostationary Lightning Mapper (GLM) detect lightning based on transient changes in cloud illumination. The horizontal location of lightning is determined from the coordinates of the pixels on the imaging array illuminated during the flash. However, the vertical position of the lightning pulses (approximated by GLM “groups”) below the cloud top cannot be routinely measured from a single space-based instrument. In our prior work, we have developed a machine learning algorithm that can infer optical source altitude for a given pulse based on how the optical energy is distributed across the group footprint and the local Advanced Baseline Imager Cloud-Top Height (CTH). In this fourth part of our thundercloud illumination study, we leverage these source altitudes to generate volumetric GLM imagery of a Colombia thunderstorm. We find that 3D versions of the current GLM meteorological imagery products (that describe thunderstorm kinematics) and thundercloud imagery products (that depict how the flashes appear from space) provide additional insights into lightning activity in the thunderstorm that are lost in the vertical integration used to generate the current 2D GLM gridded products. This new volumetric imaging capability provides a more comprehensive picture of where lightning occurs in the storm, how its physical characteristics vary across three-dimensional space, and how its optical emissions interact with surrounding the cloud medium.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A new generation of uranium coincidence fast neutron collars for assay of LWR fresh fuel assemblies

The active uranium neutron coincidence collar provides a means of non-destructively assaying the fissile linear density of Light Water Reactor fresh fuel assemblies containing low enriched uranium. These neutron collars can operate in two modes: a thermal and a fast mode. In fast mode, a neutron collar has an added cadmium (Cd) liner in the sample cavity of the detector to reduce the impact of the burnable poison (thermal neutron absorber) on the detector signal (doubles). The main advantage for operating in fast mode is a detected signal that is less dependent of the burnable neutron poison content and thus less dependent on facility operator declarations. The drawback is that operating in fast mode requires a longer measurement time (~hour vs tens of minutes for thermal mode) to achieve the statistically needed precision in the measurements. The trend in the modern reactor fuel assemblies is moving to higher burnup by using higher initial enrichment and, consequently, a higher number of burnable poison rods to compensate the initial neutron reactivity. The increase of the burnable poison loading has motivated the development of a new generation of high efficiency fast neutron collars to allow practical measurements in-field by nuclear inspectors. This paper describes the development and performance evaluation of a new generation of neutron collars, for both boiling water reactor (BWR) and pressurized water reactor (PWR) fuels, jointly developed between the US Department of Energy, through Los Alamos National Laboratory, and the Euratom Safeguards Directorate of the European Commission. In this work, we present here calibrations with reference fuel assemblies at Los Alamos National Laboratory as well as the results of in-field measurement campaigns in fuel fabrication plants with modern commercial fuel assemblies. The experimental results show that a typical PWR verification can be made in a total time of 30 min with an uncertainty in the measured mass of 2% at one standard deviation (1σ). A BWR verification can be made in 47 min with an uncertainty in the measured mass of 1.9% at 1σ, or a total time of 20 min with 1σ uncertainty in the measured mass of 2.5%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Unveiling the Electrocatalytic Hydrogen Evolution Reaction Pathway on RuP 2 through Ab Initio Grand Canonical Monte Carlo

In this study, the high catalytic reactivity of ruthenium phosphide (RuP 2 ) has been identified by first-principles density functional theory (DFT) calculations for the electrocatalytic hydrogen evolution reaction (HER). Complex surface reconstructions are considered by applying the ab initio grand canonical Monte Carlo (ai-GCMC) algorithm, efficiently providing a sufficient phase-space exploration of possible surfaces. Combined with surface-phase Pourbaix diagrams, we are able to identify the actual surfaces that obtained under specific experimental environments, thus leading to a more accurate understanding of the nature of the active sites and the binding strength of adsorbates. Specifically, through hundreds of surface reconstructions and hydrogenation states generated with ai-GCMC, we identify the most favorable surface phases of RuP 2 under aqueous acidic conditions. We discover that the HER activity is determined by multiple surfaces with different stoichiometries within a narrow electrode potential window. Low HER overpotential (η) has been found for each of the identified surfaces, as low as 0.04 V. High H-coverage reconstructed surfaces have been discovered under acidic conditions, and the surface Ru sites introduced by additional Ru adatoms or exposed by P-vacancies serve as the active sites for HER based on their nearly reversible H binding. Furthermore, this work provides atomistic insights into the origin of high HER activity on RuP 2 by exploring the dynamic surface phases of electrocatalysts and features a generalizable method to explore the reconstructed/hydrogenated surface space as a function of experimental conditions.

25 ENERGY STORAGE↗
DOE OSTI2020

Radical Chemistry and Reaction Mechanisms of Propane Oxidative Dehydrogenation over Hexagonal Boron Nitride Catalysts

Although hexagonal boron nitride (h-BN) has recently been identified as a highly efficient catalyst for the oxidative dehydrogenation of propane (ODHP) reaction, the reaction mechanisms, especially regarding radical chemistry of this system, remain elusive. Now, the first direct experimental evidence of gas-phase methyl radicals (CH 3 . ) in the ODHP reaction over boron-based catalysts is achieved by using online synchrotron vacuum ultraviolet photoionization mass spectroscopy (SVUV-PIMS), which uncovers the existence of gas-phase radical pathways. Combined with density functional theory (DFT) calculations, the results demonstrate that propene is mainly generated on the catalyst surface from the C-H activation of propane, while C 2 and C 1 products can be formed via both surface-mediated and gas-phase pathways. These observations provide new insights towards understanding the ODHP reaction mechanisms over boron-based catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Stable Cycling with Intimate Contacts Enabled by Crystallinity–Controlled PTFE–Based Solvent–Free Cathodes in All–Solid–State Batteries

All-solid-state batteries (ASSBs) employing Li-metal anodes and inorganic solid electrolytes are attracting great attention due to high safety and energy density for next-generation energy storage devices. However, the volume change of cathode active materials can cause contact loss, resulting in charge carrier isolation, heterogeneous current distribution, and poor electrochemical properties in ASSBs. Here, a simple, yet effective, solvent-free electrode engineering approach with polytetrafluoroethylene (PTFE) as a binder for ASSBs is reported, enabling intimate contact and stable interfaces with the cathode. It is substantiated that the crystallinity of PTFE can be controlled depending on the heat history, and highly crystalline PTFE displays robust mechanical properties. High-nickel LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathode prepared with crystalline PTFE show improved cycle and rate performances in ASSBs. In addition, it is revealed that the intimate contact between cathode particles with a stable cathode electrolyte layer is maintained during cycling by postmortem studies. Furthermore, this simple engineering method can be applied to prepare cathodes with a variety of active materials and solid electrolytes in ASSBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Direct observation of annealing-driven recrystallization behavior in magnesium alloy at low strain condition

Grain-twin interactions are significant in texture modification under thermodynamic driving force. In this study, annealing-driven twinning/detwinning behavior, grain growth, and corresponding texture evolution in a pre-deformed AZ31 magnesium alloy were systematically tracked and investigated via in-situ heating synchrotron X-ray diffraction and quasi in-situ electron backscattered diffraction techniques. A twinning texture is generated in the pre-deformed sample due to the activation of {$10\bar{1}2$} tensile twinning. During annealing, dislocation annihilation occurs between 100 and 280 °C, and recrystallization occurs above 280 °C, manifesting as the initial residual matrix and twins being competitively swallowed by each other, forming a bimodal texture. The recrystallization process is completed by boundary movement, which depends on the energy difference across the boundary. In addition, it is found that the grain boundaries favor movement towards the side with higher stored energy, regardless of the boundary type or the boundary energy.

In-situ observation↗
DOE OSTI2020

Oxygen-Based Anion Redox for Lithium Batteries

The importance of current Li-ion batteries (LIBs) in modern society cannot be overstated. While the energy demands of devices increase, the corresponding enhancements in energy density of battery technologies are highly sought after. Currently, many different battery concepts, such as Li-S and metal-air among many others, have been investigated. However, their practical implementation has mostly been restricted to the prototyping stage. In fact, most of these technologies require rework of existing Li-ion battery manufacturing facilities and will naturally incur resistance to change from industry. For this reason, one specifically attractive technology, anionic redox in transition metal oxides, has gained much attention in the recent years. Its ability to be directly used in already established processes and higher energy density with similar electrolyte formulation make it a key materials research direction for next generation Li-ion batteries. In regular LIBs, the redox active centers are the transition metal cation. In anion redox, both the anion (typically O) and the transition metal cation are utilized as redox centers with enormous implications for increasing energy density. This new material can be highly competitive for replacing the current LIB technologies. However, much is still unknown about its cyding mechanism. Upon activating the O redox couples, most cationic and anionic redox active materials will either evolve O 2 or undergo irreversible structural degradation with associated severe decreases in electrochemical performance. By understanding the transition from full anion redox to partial cationic and anionic redo; we hope readers can gain a deeper understanding of the topic. This Account will focus mainly on the work that was conducted by our group at Argonne National Laboratory. Herein, the phenomenon of cationic and anionic redox in a lithium-ion battery cathode will first be discussed. Our work in resonant inelastic X-ray scattering to investigate the spectroscopic features of O after delithiation has found potential "fingerprint" signals that could likely be used to identify and confirm reversible O redox if corroborated with other techniques. To follow, we will examine our work on Li-O 2 batteries. While our group and the research community have had many significant contributions and improvements to the field of Li-O 2 (such as decreasing overpotential and achieving cyclability in air environment), its practical application is still far from realization. Perhaps our most important contribution to this area is the discovery that Ir deposited on reduced graphene oxide can be used to halt the reduction of O 2 at the LiO 2 oxidation state. This not only significantly decreases the charge overpotential but also presents the important concept of oxidation-state controlled discharge. Subsequently, we will focus on our oxidation state-controlled redox-based charging of oxygen in a pure oxygen redox Li-ion battery. Future implications of this technology will be emphasized.

25 ENERGY STORAGE↗
DOE OSTI2025

Chemo-Mechanical Behavior and Stability of High-Loading Cathodes in Solid-State Batteries

Solid-state batteries can offer higher energy density and improved safety compared to lithium ion batteries, which use flammable liquid electrolytes. Increasing the ratio of cathode active materials in composite cathodes enhances the energy density and reduces manufacturing costs. Changes in the ratio of cathode active materials alter the microstructure and chemo-mechanical response of a cathode during operation. Understanding the relationship between composition, microstructure, and chemo-mechanical interactions is critical for optimizing solid-state cathodes. Here, in this study, we engineered composite cathodes with varying ratios of LiNi 0.8 Co 0.1 Mn 0.1 O 2 and Li 6 PS 5 Cl to systematically investigate the role of microstructural evolution in long-term chemo-mechanical transformations. Chemo-mechanical stresses resulting from the volume changes of the cathode active materials led to degradation mechanisms, such as fracture and interfacial delamination. Active material fracture and delamination led to underutilization of active material and significant capacity decay during cycling. Coatings that suppress active material-active material interactions during cycling may aid in suppressing the generation of local stress hotspots.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Abstract Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

97 MATHEMATICS AND COMPUTING↗