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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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Here, understanding the nature of structural evolution of Cu nanocatalysts during CO 2 electroreduction is a long-standing question in catalysis science. Now, operando electron microscopy techniques correlated with X-ray spectroscopy and first-principles modelling demonstrate that the reaction products themselves drive the structural evolution in Cu nanocubes.
Atomic layer-by-layer molecular beam epitaxy (ALL-MBE) is a sophisticated technique to synthesize high-temperature superconductor (HTS) materials. ALL-MBE produces single-crystal HTS films with atomically smooth surfaces and interfaces, as well as precise multilayer heterostructures engineered down to a single atomic layer level. This enables the fabrication of tunnel junctions, nanowires, nanorings, and other HTS devices of interest. Our group has focused on ALL-MBE synthesis and materials science of La 2–x Sr x CuO 4 (LSCO), a representative HTS cuprate. In the past two decades, we have synthesized over three thousand LSCO thin films and characterized them by a range of analytical techniques. Here, we present in full detail a systematic process for the synthesis and engineering of atomically perfect LSCO films. The procedure includes the preparation of substrates, calibration of the elemental sources, the recipe for ALL growth of LSCO films without any secondary-phase precipitates, post-growth annealing of the films, and ex situ film characterization. This report should aid replication and dissemination of this technique of synthesizing single-crystal LSCO films for basic research as well as for HTS electronic applications.
We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a metric of the amount of unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitutes a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy two-qubit processor.
Nanoparticle silicon–graphite composite electrodes are a viable way to advance the cycle life and energy density of lithium-ion batteries. However, characterization of composite electrode architectures is complicated by the heterogeneous mixture of electrode components and nanoscale diameter of particles, which falls beneath the lateral and depth resolution of most laboratory-based instruments. In this work, we report an original laboratory-based scanning probe microscopy approach to investigate composite electrode microstructures with nanometer-scale resolution via contrast in the electronic properties of electrode components. Applying this technique to silicon-based composite anodes demonstrates that graphite, SiOx nanoparticles, carbon black, and LiPAA binder are all readily distinguished by their intrinsic electronic properties, with measured electronic resistivity closely matching their known material properties. Resolution is demonstrated by identification of individual nanoparticles as small as ~20 nm. Our technique presents future utility in multiscale characterization to better understand particle dispersion, localized lithiation, and degradation processes in composite electrodes for lithium-ion batteries.
Accurate characterization of two ultrashort laser pulses is of great interest in many ultrafast pump-probe experiments. We demonstrate a method based on four-wave mixing (FWM) in a gas which could be easily implemented into many existing pump-probe setups with minimal modifications for accurate, in situ characterization of both unknown pulses. This technique is tested on pairs of unknown pulses at wavelengths of 400/800 nm, and 266/400 nm. We measured the spectrogram of the pulse generated through FWM of the two unknown pulses by scanning the delay between two unknown pulses. The retrieval algorithm converges to accurately predict the intensity and the phase profiles of both unknown pulses with a trace error of < 1% and the accuracy is verified using an independent pulse characterization device.
Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution. Among 14 patients selected in this study, 7 patients with Type B and C lesions were included in the complex lesion group including ostial, bifurcation, serial lesions and lesion where flow was supplied by collateral bed. Simple lesion group included 7 patients with lesions that were discrete, <10mm long and readily accessible. Intracoronary assessment was performed using CFD-CA framework and validated by comparing to clinically measured pressure-based index, such as FFR. Local pressure, endothelial shear stress (ESS) and velocity profiles were derived for all patients. We validates the accuracy of our CFD-CA framework and report excellent agreement with invasive measurements (n=14,R 2 =0.6,p=0.0013). Ultra-high resolution achieved by the model enable physiological assessment in complex lesions and quantify hemodynamic metrics in all vessels up to 1mm in diameter. Importantly, we demonstrate that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states [n = 14, p=0.03]. Higher ESS was observed in the complex lesion group (7.0±4.7 Pa) than in simple lesion group (4.8±3.6 Pa). Complex coronary lesions have higher ESS compared to simple lesions, such differential hemodynamic evaluation can provide much the needed insight into the increase in adverse outcomes for such patients and has incremental prognostic value over traditional pressure-based indices, such as FFR.
Magnets with isotropic easy-plane symmetry host Goldstone modes that can be leveraged for efficient spin transport. Here, we present a time-resolved optical polarimetry technique that allows us to detect and characterize such low-frequency modes, and use it to observe the Goldstone mode in the multi-Q broken helix phase of EuIn2As2. The strength of our technique comes from the ability to distinguish between nematic and magnetization dynamics in order to yield information about the mode structure, in addition to its frequency. We find that the nearly uniform spin precession characteristic of a Goldstone mode is realized only when a small magnetic field is used to unpin the broken helix from local strain generated during crystal growth. In this regime, the mode frequency scales linearly with the applied field due to the ground state C2z symmetry of the broken helix. Our work shows how optical polarimetry can be used to study the Goldstone modes of complex magnets.
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Gamma survey techniques are an especially powerful decommissioning tool at naturally occurring radioactive material (NORM) sites due to both the low cost to obtain data over a large spatial scale and the abundance of gamma emitters in the uranium and thorium decay series. Gamma surveys are executed by coupling a detector - most often a sodium iodide crystal - to a global positioning system (GPS), then reporting a location and gross gamma reading coincidentally to a data logger. Systems may be carried by workers or mounted to a car, all-terrain vehicle, or unmanned aerial system (UAS). The resulting data set provides a high-resolution but low precision map of the gamma radiation field over the area surveyed. Frequently this map is also correlated to soil concentrations of NORM radionuclides (most often, Ra-226) and/or exposure rate. Gamma survey parameters such as movement speed, transect spacing, and data logging frequency define the spatial resolution of the resulting surface, and can be optimized depending on the desired survey sensitivity. This paper examines gamma survey as a tool for decommissioning NORM sites and provides an overview of current gamma survey technology designed to improve the efficiency and effectiveness of the decommissioning process. Topics to be discussed in the paper include: - An overview of gamma survey systems, and the utility of different delivery vehicles depending on desired cost, desired spatial resolution, and site topography. - The influence of physical detector characteristics on detection sensitivity and survey planning. - The tradeoff between high-resolution and large spatial extent, but inherently uncertain data, and low-resolution, low spatial extent, but highly certain data, as well as the specific utility of each of these types of data during NORM facility decommissioning. - Confounding variables that may limit the utility of gamma survey at some sites (e.g., radon gas and spatial heterogeneity / hot spots), and methods to plan for and control these conditions. Results show that the confounding variables, such as radon and data output can greatly influence the overall data quality associated with the decommissioning process. In addition, the use of real-time and aerial survey platforms provides a method for ensuring proper spatial extent of the data. When applied thoughtfully, gamma survey is a powerful tool for detecting NORM radionuclides in the environment and a cost-effective technique for identifying areas requiring remediation. However, entities performing or using gamma survey as a decommissioning tool must be aware of both its advantages and its limitations before basing remediation or regulatory action on gamma survey results. (authors)
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Mitigating and adapting to climate change requires decarbonizing electricity while ensuring resilience of supply, since a warming planet will lead to greater extremes in weather and, plausibly, in power outages. Although it is well known that long-duration outages severely impact economies, such outages are usually not well characterized or modeled in grid infrastructure planning tools. Here, we bring together data and modeling techniques and show how they can be used to characterize and model long-duration outages. We illustrate how to integrate outages in planning tools for one promising mode of resilient energy supply—microgrids. Failing to treat these extremes in models can lead to microgrid designs (1) that do not realize their full value of resilience, since models do not see the benefits of protecting against extremes, and (2) that appear reliable on paper yet do not actually protect against extremes. Although utilities record power interruptions, lack of access to that data is hindering research on resilience; making datasets available publicly would substantially aid efforts to improve grid planning tools.
A series of supported electrophilic organoruthenium complexes has been synthesized via surface organometallic chemistry (SOMC) techniques and applied to the selective hydrosilylation of olefins. The air-sensitive 16e - complex Cp*RuMes(PCy 3 ) (1) (Cp* = pentamethylcyclopentadienyl, Mes = mesityl) was synthesized by the treatment of Cp*RuCl(PCy 3 ) with mesityl Grignard MesMgBr. This species was chemisorbed onto sulfated zirconia SO 4 /ZrO 2 , but the resulting material was inactive toward cyclohexene hydrosilylation with phenylsilane. Instead, Cp*RuMes(PCy 3 ) was treated with phenylsilane (PhSiH 3 ) to provide a ruthenium disilyl hydride complex Cp*RuH(SiH 2 Ph) 2 (PCy 3 ) (3), which was fully characterized by NMR spectroscopy and single-crystal X-ray diffraction. Grafting this species onto SO 4 /ZrO 2 resulted in the formation of phenylsilane along with the surface electrophilic species [Cp*RuH(R)(X-SiHPh)(PCy 3 )] (R = H, O 3 S-O or O 3 Zr-O; 4a, 4b, X = O 3 S-O, and O 3 Zr-O, respectively) as the major species. Material 4 was characterized via a combination of spectroscopic techniques including dynamic nuclear polarization (DNP)-enhanced solid-state NMR spectroscopy, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), X-ray absorption spectroscopy (XAS), and density function theory (DFT) calculations. Further, capping the remaining acid sites on 4 with Me 3 Si-SiMe 3 provides 5, which significantly reduces side reactions, such as olefin isomerization and silane redistribution. Catalyst 5 is a highly robust and selective hydrosilylation catalyst and can be recycled up to 5 times without significant diminishment of activity. Exclusive anti-Markovnikov regiochemistry, cis-addition selectivity, and the inactivity of secondary and tertiary silanes provide support for the proposed Glaser-Tilley mechanism involving cationic ruthenium silylene species analogous to homogeneous systems.
The shielded highly enriched uranium (HEU) problem is one that still needs to be addressed. As a first step towards a solution, the Snyder shells (depleted uranium) were actively interrogated with a SEA-7 betatron in various configurations using an MC15 as a detector. These configurations include masses from 0.55 to 5.1 kg, as well as 5.1 kg in a steel container. A strong electromagnetic field (EMF) background was identified in previous work, but the results in this work show all configurations produce a signal well above the background. An initial simulation capability was developed that produces the expected flux profile from the SEA-7 betatron, but further work needs to be done to determine the magnitude of the flux via a combination of experiment and simulation validation. Depleted uranium generally produces more neutrons than HEU, so additional future work will need to include similar measurements of HEU to confirm similar performance of the technique. Additionally the EMF background needs to be better characterized as a function of distance and angle from the SEA-7 source and mitigation techniques explored.
Meteorites can contain a wide range of material phases due to the extreme environments found in space and are ideal candidates to search for natural superconductivity. However, meteorites are chemically inhomogeneous, and superconducting phases in them could potentially be minute, rendering detection of these phases difficult. To alleviate this difficulty, we have studied meteorite samples with the ultrasensitive magnetic field modulated microwave spectroscopy (MFMMS) technique [J. G. Ramírez, A. C. Basaran, J. de la Venta, J. Pereiro, I. K. Schuller, Rep. Prog. Phys. 77, 093902 (2014)]. Here, we report the identification of superconducting phases in two meteorites, Mundrabilla, a group IAB iron meteorite [R. Wilson, A. Cooney, Nature 213, 274–275 (1967)] and GRA 95205, a ureilite [J. N. Grossman, Meteorit. Planet. Sci. 33, A221–A239 (1998)]. MFMMS measurements detected superconducting transitions in samples from each, above 5 K. By subdividing and remeasuring individual samples, grains containing the largest superconducting fraction were isolated. The superconducting grains were then characterized with a series of complementary techniques, including vibrating-sample magnetometry (VSM), energy-dispersive X-ray spectroscopy (EDX), and numerical methods. Finally, these measurements and analysis identified the likely phases as alloys of lead, indium, and tin.
Localized residual stress and elastic strain concentrations in microelectronic devices often affect the electronic performance, resistance to thermomechanical damage, and, likely, radiation tolerance. A primary challenge for the characterization of these concentrations is that they exist over sub-μm length-scales, precluding their characterization by more traditional residual stress measurement techniques. Here, in this study, we demonstrate the use of synchrotron x-ray-based differential aperture x-ray microscopy (DAXM) as a viable, non-destructive means to characterize these stress and strain concentrations in a depth-resolved manner. DAXM is used to map two-dimensional strain fields between the source and the drain in a gallium nitride (GaN) layer within high electron mobility transistors (HEMTs) with sub-μm spatial resolution. Strain fields at various positions in both pristine and irradiated HEMT specimens are presented in addition to a preliminary stress analysis to estimate the distribution of various stress components within the GaN layer. γ-irradiation is found to significantly reduce the lattice plane spacing in the GaN along the sample normal direction, which is attributed to radiation damage in transistor components bonded to the GaN during irradiation.
To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.