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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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Evolution of 15 solar masses star from main sequence through helium core burning, comparing with evolution of less massive stars
Complex metal hydrides provide high-density hydrogen storage, which is essential for vehicular applications. However, the utility of these materials has been limited by thermodynamic and kinetic barriers present during the dehydrogenation and rehydrogenation processes as new phases form inside parent phases. Better understanding of the mixed-phase mesostructures and their interfaces may assist in improving cyclability. In this work, the evolution of the phases during hydrogenation of lithium nitride and dehydrogenation of lithium amide with lithium hydride are probed with scanning-transmission X-ray microscopy at the nitrogen K edge. With this technique, intriguing core-shell structures were observed in particles of both partially hydrogenated Li 3 N and partially dehydrogenated LiNH 2 + 2 LiH. The potential contributions of both internal hydrogen mobility and interfacial energies on the generation of these structures are discussed.
Abstract Developing highly efficient, cost effective, and environmentally friendly electrocatalysts for the oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and hydrogen evolution reaction (HER) is of interest for sustainable and clean energy technologies, including metal–air batteries and fuel cells. In this work, the screening of electrocatalytic activities of a series of single metallic iron, cobalt, and nickel nanoparticles and their binary and ternary alloys encapsulated in a graphitic carbon shell toward the OER, ORR, and HER in alkaline media is reported. Synthesis of these compounds proceeds by a two‐step sol–gel and carbothermal reduction procedure. Various ex situ characterizations show that with harsh electrochemical activation, the graphitic shell undergoes an electrochemical exfoliation. The modified electronic properties of the remaining graphene layers prevent their exfoliation, protect the bulk of the metallic cores, and participate in the electrocatalysis. The amount of near‐surface, higher‐oxidation‐state metals in the as‐prepared samples increases with electrochemical cycling, indicating that some metallic nanoparticles are not adequately encased within the graphite shell. Such surface oxide species provide secondary active sites for the electrocatalytic activities. The Ni–Fe binary system gives the most promising results for the OER, and the Co–Fe binary system shows the most promise for the ORR and HER.
Quantum algorithms on the noisy intermediate-scale quantum (NISQ) devices are expected to simulate quan- tum systems that are classically intractable to demonstrate quantum advantages. However, the non-negligible gate error on the NISQ devices impedes the conventional quantum algorithms to be implemented. Practical strategies usually exploit hybrid quantum-classical quantum algorithms to demonstrate potentially useful ap- plications of quantum computing in the NISQ era. Among the numerous hybrid quantum-classical algorithms, recent efforts highlight the development of quantum algorithms based upon quantum computed Hamiltonian moments, ?f|Hˆn|f? (n = 1, 2, · · · ), with respect to quantum state |f?. In this tutorial, we will give a brief review of these quantum algorithms with focuses on the typical ways of computing Hamiltonian moments using quantum hardware and improving the accuracy of the estimated state energies based on the quantum computed moments. Furthermore, we will present a tutorial to show how we can measure and compute the Hamiltonian moments of a four-site Heisenberg model, and compute the energy and magnetization of the model utilizing the imaginary time evolution in the real IBM-Q NISQ hardware environment. Along this line, we will further discuss some practical issues associated with these algorithms. We will conclude this tutorial review by overviewing some possible developments and applications in this direction in the near future.
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Due to the large formation energy of vacancies and noble gas atoms in the form of interstitials or substitutional atoms in nuclear fuel (UO2), the thermodynamic equilibrium concentrations of these species are very low in the nuclear fuel matrix even at very high temperatures, which imposes difficulties upon the quantitative study of bubble evolution via the phase-field method. In this study, a quantitative phase-field model is proposed to deal with this problem. The system’s free energy density is derived according to the principles of thermodynamics, with consideration of the elastic effect and with the use of real material parameters from experiments. The model is useful for the study of the kinetics of gas bubble growth with very dilute concentrations of vacancy and gas atoms in the matrix. This model is applied to study single bubble growth and multiple bubble growth under various concentrations of vacancy and gas atoms and at various temperatures. The elastic effect and the effects of the generation rate of vacancies and gas atoms on bubble growth are analyzed.
Accelerating the nuclear fuel qualification process will rely on some combination of advanced modeling and simulation techniques with accelerated irradiation testing and separate effects experiments to enable the development of new fuel concepts in a shorter time frame. One of the key challenges to successfully leveraging accelerated irradiation tests will be understanding the artifacts that may be introduced with accelerated accumulation of dose and/or burnup. This work presents phase field (MARMOT) simulations of the evolution of representative 2D UO2 microstructures up to 40 MWd/kgU. Simulations were performed under both commercial light water reactor fuel conditions as well as those that would be expected for highly accelerated (~10x) burnup conditions similar to those used in the MiniFuel irradiations in Oak Ridge National Laboratory’s High Flux Isotope Reactor. The phase field model was coupled with a discrete nucleation algorithm to model re- structuring at high burnup. The effect of the different fission rates in both microstructures was investigated at two temperatures: 650?C and 800?C. The lower temperature simulations both showed an onset of restructuring at nearly 60 MWd/kgU. More extensive restructuring was obtained in the MiniFuel microstructure compared with that of the PWR fuel. At 800?C, no restructuring was obtained as a result of the thermally activated diffusion of Xe atoms and U vacancies to fission gas bubbles, which reduces the nucleation driving force. These results highlight the importance of using modeling and simulation tools to inform the environmental conditions during targeted accelerated irradiation tests to extract the most useful fuel performance data.
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Metal phosphide-containing materials have emerged as a potential candidate of non-precious metal-based catalysts for alkaline oxygen evolution reaction (OER). While it is known that metal phosphide undergoes structural evolution, considerable debate persists regarding the effects of dynamics on the surface activation and morphological stability of the catalysts. In this study, we synthesize NiP x -FeO x core-shell nanocatalysts with an amorphous NiP x core designed for enhanced OER activity. Using ex-situ X-ray absorption spectroscopy, we elucidate the local structural changes as a function of cyclic voltammetry cycles. Our studies suggest that the presence of corner-sharing octahedra in the FeO x shell improves structural rigidity through interlayer cross-linking, thereby inhibiting the diffusion of OH - /H 2 O. Thus, the FeO x shell preserves the amorphous NiP x core from rapid oxidation to Ni 3 (PO 4 ) 2 and Ni(OH) 2 . On the other hand, the incorporation of Ni from the core into the FeO x shell facilitates absorption of hydroxide ions for OER. As a result, the Ni/Fe(OH) x at the surface oxidizes to the active γ-(oxy)hydroxide phase under the applied potentials, promoting OER. This intriguing synergistic behavior holds significance as such synthetic route involving the FeO x shell can be extended to other systems, enabling manipulation of surface adsorption and diffusion of hydroxide ions. These findings also demonstrate that nanomaterials with core-shell morphology can be tuned to leverage the strength of each metallic component for improved electrochemical activities.
Perovskite oxides are an important class of oxygen evolution reaction (OER) catalysts in alkaline media, despite the elusive nature of their active sites. In this work, we demonstrate that the origin of the OER activity in a La 1-x SrxCoO 3 model perovskite arises from a thin surface layer of Co hydr(oxy)oxide (CoO x H y ) that interacts with trace-level Fe species present in the electrolyte, creating dynamically stable active sites. Generation of the hydr(oxy)oxide layer is a consequence of a surface evolution process driven by the A-site dissolution and O-vacancy creation. In turn, this imparts a 10-fold improvement in stability against Co dissolution and a 3-fold increase in the activity-stability factor for CoO x H y / LSCO when compared to nanoscale Co-hydr(oxy)oxides clusters. Our results suggest new design rules for active and stable perovskite oxide-based OER materials.
Silver–copper (AgCu) bimetallic catalysts hold great potential for electrochemical carbon dioxide reduction reaction (CO 2 RR), which is a promising way to realize the goal of carbon neutrality. Although a wide variety of AgCu catalysts have been developed so far, it is relatively less explored how these AgCu catalysts evolve during CO 2 RR. The absence of insights into their stability makes the dynamic catalytic sites elusive and hampers the design of AgCu catalysts in a rational manner. Here, in this study, we synthesized intermixed and phase-separated AgCu nanoparticles on carbon paper electrodes and investigated their evolution behavior in CO 2 RR. Our time-sequential electron microscopy and elemental mapping studies show that Cu possesses high mobility in AgCu under CO 2 RR conditions, which can leach out from the catalysts by migrating to the bimetallic catalyst surface, detaching from the catalysts, and agglomerating as new particles. Besides, Ag and Cu manifest a trend to phase-separate into Cu-rich and Ag-rich grains, regardless of the starting catalyst structure. The composition of the Cu-rich and Ag-rich grains diverges during the reaction and eventually approaches thermodynamic values, i.e., Ag 0.88 Cu 0.12 and Ag 0.05 Cu 0.95 . The separation between Ag and Cu has been observed in the bulk and on the surface of the catalysts, highlighting the importance of AgCu phase boundaries for CO 2 RR. In addition, an operando high-energy-resolution X-ray absorption spectroscopy study confirms the metallic state of Cu in AgCu as the catalytically active sites during CO 2 RR. Taken together, this work provides a comprehensive understanding of the chemical and structural evolution behavior of AgCu catalysts in CO 2 RR.
Structural evolution occurs upon A-site partial substitution of Sr by Yb in SrCoO 3−δ , leading to enhanced performance and stability.
This article discusses likely future contexts of, and options for, global threat-reduction activities to support nonproliferation goals over the next five to ten years. Threat-reduction activities span a continuum from unilateral actions that the United States might take with little cooperation and transparency at one end to cooperative actions associated with negotiated treaties and agreements at the other. This study focuses on cooperative approaches embodied in the Cooperative Threat Reduction (CTR) program, which has been the most visible program reducing the threats posed by weapons of mass destruction for over two decades. Here, we argue that CTR’s evolution can be described in terms of the relationship between the desired US influence on outcomes, the ability to generate a common threat definition, and appetite for collaboration on threat reduction. To that end, this article provides an introduction and overview of CTR initiatives over its twenty-seven-year history and a review of relevant legislation and trends. After introducing and describing the CTR Possible Futures Framework, this article offers five possible options for—and discusses the implications of—CTR’s future evolution.
We study the evolution of cosmological perturbations in dark-matter models with elastic and velocity-independent self interactions. Such interactions are imprinted in the matter-power spectrum as dark acoustic oscillations, which can be experimentally explored to determine the strength of the self scatterings. Models with self interactions have similarities to warm dark matter, as they lead to suppression of power on small scales when the dark-matter velocity dispersion is sizable. Nonetheless, both the physical origin and the extent of the suppression differ for self-interacting dark matter from conventional warm dark matter, with a dark sound horizon controlling the reduction of power in the former case, and a free-streaming length in the latter. We thoroughly analyze these differences by performing computations of the linear power spectrum using a newly developed Boltzmann code. We find that while current Lyman-α data disfavor conventional warm dark matter with a mass less than 5.3 keV, when self interactions are included at their maximal value consistent with bounds from the Bullet Cluster, the limits are relaxed to 4.4 keV. Lastly, we make use of our analysis to set novel bounds on light scalar singlet dark matter.
Abstract Energy, water, and agricultural resources across the globe are highly interconnected. This interconnectivity poses science challenges, such as understanding and modeling interconnections, as well as practical challenges, such as efficiently managing interdependent resource systems. Using the US as an example, this study seeks to define and explore how interconnectivity evolves over space and time under a range of influences. Concepts from graph theory and input–output analysis are used to visualize and quantify key intersectoral linkages using two new indices: the ‘Interconnectivity Magnitude Index’ and the ‘Interconnectivity Spread Index’. Using the Global Change Analysis Model (GCAM-USA), we explore the future evolution of these indices under four scenarios that explore a range of forces, including socioeconomic and technological change. Analysis is conducted at both national and state level spatial scales from 2015 to 2100. Results from a Reference scenario show that resource interconnectivity in the US is primarily driven by water use amongst different sectors, while changes in interconnectivity are driven by a decoupling of the water and electricity systems, as power plants become more water-efficient over time. High population and GDP growth results in relatively more decoupling of sectors, as a larger share of water and energy is used outside of interconnected sector feedback loops. Lower socioeconomic growth results in the opposite trend. Transitioning to a low-carbon economy increases interconnectivity because of the expansion of purpose-grown biomass, which strengthens the connections between water and energy. The results highlight that while some regions may experience similar sectoral stress projections, the composition of the intersectoral connectivity leading to that sectoral stress may call for distinctly different multi-sector co-management strategies. The methodology we introduce here can be applied in diverse geographical and sectoral contexts to enable better understanding of where, when, and how coupling or decoupling between sectors could evolve and be better managed.
Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.
Yarrowia lipolytica , an oleaginous yeast, shows promise for industrial fermentation due to its robust acetyl-CoA flux and well-developed genetic engineering tools. However, its lack of an active xylose metabolism restricts the conversion of cellulosic sugars to valuable products. To address this, metabolic engineering, and adaptive laboratory evolution (ALE) were applied to the Y. lipolytica PO1f strain, resulting in an efficient xylose-assimilating strain (XEV). Whole-genome sequencing (WGS) of the XEV followed by reverse engineering revealed that the amplification of the heterologous oxidoreductase pathway and a mutation in the GTPase-activating protein gene (YALI0B12100g) might be the primary reasons for improved xylose assimilation in the XEV strain. When a sorghum hydrolysate was used, the XEV strain showed superior xylose consumption and lipid production compared to its parental strain (X123). This study advances our understanding of xylose metabolism in Y. lipolytica and proposes effective metabolic engineering strategies for optimizing lignocellulosic hydrolysates.
Surface quality plays an important role in the performance of SRF cavities. We report the topographic evolution of H degassed, N and O doped Nb surfaces subjected to electropolishing using atomic force microscopy. N doped Nb shows a substantial increase in roughness due to formation of niobium nitrides into the surface. Implications of the topography on magnetic field enhancement and vortex entry are discussed.