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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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We will develop structural mechanics models that capture the evolution of feedstock attributes during the Forest Concepts comminution process. Models will be validated by data provided by Forest Concepts and experiments performed by synergistic projects within the Feedstock Conversion Interface Consortium.
Uranium carbides with refractory metal additions are considered for Gen IV nuclear reactors and nuclear thermal propulsion as fuels for their high-temperature and corrosion resistant properties. Understanding kinetic effects that dictate microstructural evolution during fabrication and operating conditions is essential to advance technological development of these fuels. This work presents the development of an atomic mobility database for C-Nb-U systems based off available experimental data supported with ab-initio methods. The mobility assessments and uncertainty quantification (using Markov chain Monte Carlo) were conducted in the Kawin software. Carbon diffusion is considered dominant, as metal diffusion is much slower, with niobium diffusion being even slower and rate limiting than uranium metal. We provide a comprehensive and self-consistent thermo-kinetic database that is validated by diffusion couple simulations through Kawin. In conclusion, this enables prediction of microstructural and phase evolution critical for the development and lifetime assessment of next generation nuclear fuels.
The electroplating of aluminum has represented a challenge for the coatings industry for decades. The reactivity of aluminum metal leads to a very negative reduction potential. This leads to abundant gas evolution instead of aluminum reduction in water. Current aluminum deposition technology overcomes this limitation by excluding oxygen and water from the process with rigorously dry solvent and airtight plating tanks. These adaptations yield metallic aluminum depositions with significant impurities affecting its properties. In addition, this approach requires a great capital investment and cost of process maintenance. An alternative approach to Al electrodeposition has been developed based on the the use of organic ligands to stabilize the Al3+ ion in water-based solutions. By using a ligation approach, we can create aluminum species that remain stable in water while lowering the reduction potential of the aluminum complex by 0.5V. This improvement in reduction potential gives the possibility for the deposition of aluminum without significant competition from hydrogen evolution. With this platform we can deposit highly adherent aluminum-based layers on metal surfaces. The aluminum/aluminum oxide layers appear as thin, coherent and highly adherent coatings. This aluminum/alumina layer can then be chemically treated to achieve desirable surface properties which were previously inaccessible. For example, the coating can be made hydrophobic, highly adherent to different polymer coatings or even dyed using common anodizing dyes.
The electroplating of aluminum has represented a challenge for the coatings industry for decades. The reactivity of aluminum metal leads to a very negative reduction potential. This leads to abundant gas evolution instead of aluminum reduction in water. Current aluminum deposition technology overcomes this limitation by excluding oxygen and water from the process with rigorously dry solvent and airtight plating tanks. These adaptations yield metallic aluminum depositions with significant impurities affecting its properties. In addition, this approach requires a great capital investment and cost of process maintenance. An alternative approach to Al electrodeposition has been developed based on the the use of organic ligands to stabilize the Al3+ ion in water-based solutions. By using a ligation approach, we can create aluminum species that remain stable in water while lowering the reduction potential of the aluminum complex by 0.5V. This improvement in reduction potential gives the possibility for the deposition of aluminum without significant competition from hydrogen evolution. With this platform we can deposit highly adherent aluminum-based layers on metal surfaces. The aluminum/aluminum oxide layers appear as thin, coherent and highly adherent coatings. This aluminum/alumina layer can then be chemically treated to achieve desirable surface properties which were previously inaccessible. For example, the coating can be made hydrophobic, highly adherent to different polymer coatings or even dyed using common anodizing dyes.
The viscosity of glass is the most important technological property to glass manufactures and various applications. Practically, finding an accurate equation to express the glass viscosity behavior in the entire glass transition temperature range is tremendously challenge because it spans more than ten orders of magnitude. After a brief review of existing empirical viscosity equations, this work focuses on the correlating silica viscosity behavior with the evolution of glass medium-range structure, based on the recently proposed nanoflake model. From this new model, a new equation is constructed, which correctly describes the Arrhenius-type behavior of silica viscosity η above the melting temperature Tm, and non-Arrhenius-type behavior from Tm to a critical temperature Tc. At temperature lower than Tc, the equation predicts Arrhenius-type behavior again for the viscous flow with increased activation energy. The new equation agrees with experimental data in the entire temperature spanned from extremely high to extremely low. Furthermore, the application of the new equation is shown to extend to sodium silicate glasses as well.
This milestone presents multi-scale modeling research results for additively manufactured 316 stainless steel. A combination of phase field, cluster dynamics, molecular dynamics, and density functional theory with machine learning is used, allowing for predictions of radiation-driven microstructural evolution in additively manufactured 316 stainless steel over a range of temperatures, damage rates, neutron spectra, and microstructures, and supporting the development of combined ion and neutron irradiation for material qualification. Informed by ion irradiation and neutron irradiation results across the Advanced Materials and Manufacturing Technologies program, we investigate the unique aspects of radiation-driven microstructure evolution in additively manufactured 316 stainless steel. In particular, we focus on understanding the impact of carbon concentration (varying, for example, between the 316L and 316H standards) on void formation; radiation-induced segregation at dislocation cells and grain boundaries; and the evolution of dislocation loops and network dislocation populations. We find that the unique characteristics of the additively manufactured microstructures must be accounted for in understanding the evolution of dislocation populations under thermal and irradiation conditions, such as the variation in sink strengths arising due to the variation in dislocation density. We also find that the radiation-induced segregation of Cr and Ni to grain boundaries and cell walls differs due to the differences in their defect sink biases. We also find that increasing the Ni content can slow vacancy diffusion, which may provide a mechanism for the observed reduction in transient swelling rate for austenitic Fe-Cr-Ni alloys with increasing Ni content. In addition, ion irradiations have shown that increasing carbon content in 316 SS results in a larger population of smaller voids, suggesting reduced vacancy diffusion. Our results show that the carbon content of additively manufactured 316 SS has a significant impact on the migration rate of defect clusters. The presence of carbon atoms results in carbon-vacancy trapping, significantly reducing the diffusion rate of vacancies. Carbon atoms may also be trapped near the surface of a void, which may reduce void growth by trapping vacancies that diffuse toward the void.
Simulating physical systems on quantum devices is one of the most promising applications of quantum technology. Current quantum approaches to simulating open quantum systems are still practically challenging on NISQ-era devices, because they typically require ancilla qubits and extensive controlled sequences. In this work, we propose a hybrid quantum-classical approach for simulating a class of open system dynamics called random-unitary channels. These channels naturally decompose into a series of convex unitary evolutions, which can then be efficiently sampled and run as independent circuits. The method does not require deep ancilla frameworks and thus can be implemented with lower noise costs. We implement simulations of open quantum systems up to dozens of qubits and with large channel ranks. Published by the American Physical Society 2024
Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains.
The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L -1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.
Abstract Root nodule symbiosis (RNS) is a complex trait that enables plants to access atmospheric nitrogen converted into usable forms through a mutualistic relationship with soil bacteria. Pinpointing the evolutionary origins of RNS is critical for understanding its genetic basis, but building this evolutionary context is complicated by data limitations and the intermittent presence of RNS in a single clade of ca. 30,000 species of flowering plants, i.e., the nitrogen-fixing clade (NFC). We developed the most extensive de novo phylogeny for the NFC and an RNS trait database to reconstruct the evolution of RNS. Our analysis identifies evolutionary rate heterogeneity associated with a two-step process: An ancestral precursor state transitioned to a more labile state from which RNS was rapidly gained at multiple points in the NFC. We illustrate how a two-step process could explain multiple independent gains and losses of RNS, contrary to recent hypotheses suggesting one gain and numerous losses, and suggest a broader phylogenetic and genetic scope may be required for genome-phenome mapping.
The E × B staircase is a quasi-periodic pattern of pressure profile corrugations. In this work, we present a new mechanism for E × B staircase formation that involves resonant transport versus non-resonant transport. We start from a potential vorticity evolution system and use quasi-linear theory, a model dispersion relation, and a bi-Lorentzian spectrum approximation, to construct the relation between the fluxes and the profiles. With these fluxes, we close the profile evolution equations and the extended turbulence intensity evolution equation, which together constitute a turbulence-profile evolution system. In this system, the Doppler effect from the E × B mean flow can cause resonance between trapped ion precession motion and the trapped ion mode, which drives a resonant transport contribution to the fluxes. The profiles will be flattened where the resonant transport is switched on. In contrast, for the regions of non-resonant transport, profiles are steeper. A quasi-periodic pattern of profile corrugation (the E × B staircase) spontaneously emerges in this system, which is the two states mentioned above, arranged as alternating layers in space. The feedback processes during the staircase pattern formation are identified. An estimate of the critical value of the boundary heat flux is obtained, above which the staircase formation will be triggered. An estimate scaling of the step size in the staircase pattern is obtained. The resonant turbulent transport is also a mechanism for collisionless saturation of zonal flow. Furthermore, this work is related to internal transport barrier formation and suggests some new scenarios, such as an enhanced confined L mode.
The oxygen-evolution reaction (OER) is pivotal in many energy-conversion technologies as it is an important counter reaction to others that convert stable chemicals to higher-value products using electrochemistry. The local microenvironment and pH for the anode OER can vary from acidic to neutral to alkaline depending on the system being explored, making definitive mechanistic insights difficult. In this paper, we couple experiments, first-principles calculations based on density functional theory, microkinetics, and transport modeling to explore the entire pH range of the OER. At low current densities, neutral pH values unexpectedly perform better than the acidic and alkaline conditions, and this trend is reversed at higher current densities (> 20 mA cm -2 ). Using multiscale modeling, this switch is rationalized by a change from a dual-reaction mechanism to a single rate-determining step. The model also shows how the alkaline reaction rates dominate in the middle to high pH range. Furthermore, we explore that the local pH for near-neutral conditions is much different (e.g., 2.4 at the reaction surface vs. 9 in the bulk) than the pH extremes, demonstrating the criticality that transport phenomena plays in kinetic activity.
Since even before its establishment as an independent national security laboratory in 1949, Sandia has been devoted to an overarching mission of developing advanced technologies for global peace. These technologies have taken a variety of forms, and they exist in and must address an ever-changing global security environment. An understanding of that global security environment and its possible or likely evolution is therefore critical to ensuring that Sandia can maintain its focus on strategic technology investments that will benefit the nation in the next 20- 30 years. Sandia sustains multiple Systems Analysis organizations whose responsibility includes maintaining an understanding of the global security environment as it applies across multiple mission domains. The topics below include two from Sandia's emerging threats and biodefense mission, three with relevance to Sandia's cyber defense mission, and four of particular but not exclusive relevance to Sandia's nuclear deterrence mission. All are intended to spur independent academic thought that could assist Sandia as well as the broader national security community in anticipating and adapting to a continually changing world. Sandia anticipates periodic interactions between Sandia Systems Analysis staff and SciPol Scholars Program faculty and students who choose to expand upon these topics in order to provide opportunities for feedback and communication throughout 2020-2021.
Abstract The shift towards cheaper, non‐platinum group metal electrocatalyst materials for clean energy technologies is coupled with challenges in maintaining long‐term performance. For practical purposes, electrocatalytic stability typically focuses on catalyst electrochemical performance over time. However, a deeper understanding of catalyst material property changes during operation is needed to enable material‐specific design strategies for long‐term stabilization. In the last several decades, improvements in material characterization techniques have made it possible to probe the composition, structure, and degradation products of catalysts in situ/operando. Herein we review the current understanding of in situ/operando material stability of Co‐based electrocatalysts for the oxygen evolution (OER) and reduction reactions (ORR) in acidic and alkaline environments. We focus on in situ/operando materials characterization of three categories of Co‐based OER/ORR catalysts: oxides and (oxy)hydroxides, mixed‐metal catalysts, and non‐oxide materials. This review aims to compile and compare the results from multiple studies and techniques to provide insight into the role that the starting material, pH, and applied potential have on the active surface and stability of Co‐based materials. We conclude by highlighting directions that have been underexplored with opportunities for continued research including improving in situ/operando characterization, methods to probe long‐term material changes, and the development of operando studies on full‐scale devices. Using Co‐based materials as a case study, this review shows the vital role that in situ/operando characterization must play in the future of improving electrocatalyst stability.
Integrated photoelectrochemical (PEC) devices for water splitting represent a compelling pathway for sustainable hydrogen production, directly converting solar energy into chemical fuels. While alkaline systems have achieved state-of-the-art solar-to-hydrogen (STH) efficiencies above 20% using earth-abundant catalysts, acidic PEC architectures provide unique advantages for compact device integration, fast proton transport, and stable operation under highly dynamic solar conditions. Proton-exchange membrane (PEM)-based configurations enable high current densities, low gas crossover, and rapid ionic response, making them especially well-suited for intermittent, bias-free PEC operation, despite alkaline electrolysis being more technologically mature. A critical limitation of acidic PEC systems remains, the oxygen evolution reaction (OER), which currently relies on scarce and costly iridium catalysts, restricting scalability. Here, in this study, we report a series of low-iridium mixed-metal oxide catalysts synthesized via a surfactant-assisted borohydride reduction method. An optimized Ir 0.5 Ru 0.5 O x catalyst exhibits exceptional intrinsic activity (>400 A g –1 Ir at 1.55 V vs RHE) in 0.1 M HClO 4 and maintains stable operation for over 10 days in an integrated PEC flow-cell. Sustained hydrogen production is achieved at 1.65 V with a total iridium loading of only 0.1 mg cm –2 , substantially below commercial PEM benchmarks. These results demonstrate a viable pathway toward scalable, high-performance acidic PEC hydrogen technologies.
Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. Here, we employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.
Improved sequencing technologies have profoundly altered global views of fungal diversity and evolution. High-throughput sequencing methods are critical for studying fungi due to the cryptic, symbiotic nature of many species, particularly those that are difficult to culture. However, the low coverage genome sequencing (LCGS) approach to phylogenomic inference has not been widely applied to fungi. Here we analyzed 171 Kickxellomycotina fungi using LCGS methods to obtain hundreds of marker genes for robust phylogenomic reconstruction. Additionally, we mined our LCGS data for a set of nine rDNA and protein coding genes to enable analyses across species for which no LCGS data were obtained. The main goals of this study were to: 1) evaluate the quality and utility of LCGS data for both phylogenetic reconstruction and functional annotation, 2) test relationships among clades of Kickxellomycotina, and 3) perform comparative functional analyses between clades to gain insight into putative trophic modes. In opposition to previous studies, our nine-gene analyses support two clades of arthropod gut dwelling species and suggest a possible single evolutionary event leading to this symbiotic lifestyle. Furthermore, we resolve the mycoparasitic Dimargaritales as the earliest diverging clade in the subphylum and find four major clades of Coemansia species. Finally, functional analyses illustrate clear variation in predicted carbohydrate active enzymes and secondary metabolites (SM) based on ecology, that is biotroph versus saprotroph. Saprotrophic Kickxellales broadly lack many known pectinase families compared with saprotrophic Mucoromycota and are depauperate for SM but have similar numbers of predicted chitinases as mycoparasitic.
Here, we explore three case studies of facilities at two US Department of Energy (DOE) former nuclear weapons research and production sites—the Oak Ridge National Laboratory and the Hanford site—whose risk profiles have changed during their long-term management under the DOE's surveillance and maintenance (S&M) program. These case studies provide examples of the challenges faced in communicating to external stakeholders, such as federal and state regulators, local communities surrounding the site, as well as the general public, the circumstances surrounding unexpected events or the emergence/discovery of new risk-important information at historically high-risk sites. We identify common topics of importance from these case studies and suggest a taxonomy for risk communicators to use in informing the dialogues with individuals and organizations that may not be technically oriented or fully informed on the subject matter. The taxonomy is based on technical insights from the quintessential definition of risk known as the Kaplan–Garrick “risk triplet” as well as insights from regulatory guidance documents on risk communication with external stakeholders originally developed for the commercial nuclear power industry.