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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

White-light interferometry for early-stage metal oxide growth characterization

Oxidation of metals can provide a functional oxide film or an oxide layer that can lead to material degradation causing performance limiting concerns. Because of the technological and economic impacts of oxidation, understanding early-stage oxidation growth and its mechanisms is of high importance. Unfortunately, poor empirical data during early-stage oxidation has left our current understanding of oxidation incomplete and woefully inadequate. Measurement techniques previously used for oxidation kinetics characterization include microbalance, x-ray diffraction, Fourier-transform infrared spectroscopy, and spectroscopic ellipsometry. However, these techniques lack the necessary time-resolution or vertical resolution in addition to not providing any spatial data, which is essential to early-stage oxidation studies to understand both oxidation kinetics and mechanisms. Here, a novel rapid and surface-sensitive data collection technique that provides three-dimensional spatial data of a growing oxide layer with sub-nanometer thickness resolution using white-light interferometry has been developed. This technique will be the first technique capable of providing real-time spatial results of early-stage oxidation growth. The effectiveness of this characterization tool is shown by observing the early-stage oxidation kinetics and film cracking of uranium heated to 90 °C under 500 torr of pure O 2 . Initially a parabolic oxidation growth is observed. Once the oxide layer reaches ≈ 30 nm, the epitaxial stresses between the uranium substrate and oxide film layer causes localized cracking in addition to the observed transition of the parabolic growth rate to a linear growth rate. The results of the white-light interferometry are verified by glancing incidence x-ray diffraction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

High resolution measurements of core density collapse dynamics in LHD

The core density collapse (CDC) is an abrupt instability that degrades the performance of super dense core stellarator plasmas on the Large Helical Device (LHD). In LHD, CDCs have proved to be an operational limit in so-called outward-shifted configurations. New measurements with high time resolution Thomson scattering characterize dynamics of the instability in the core. The density profile is observed to collapse inwards, narrowing the density peak till the core itself collapses. A hot, radially localized feature is also observed in the electron temperature profile during the collapse and disappears within a few hundred microseconds of CDC onset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Optimization of 3D controlled ELM-free state with recovered global confinement for KSTAR with n = 1 resonant magnetic field perturbation

Mitigation of deleterious heat flux from edge-localized modes (ELMs) on fusion reactors is often attempted with 3D perturbations of the confining magnetic fields. However, the established technique of resonant magnetic perturbations (RMPs) also degrades plasma performance, complicating implementation on future fusion reactors. Here, we introduce an adaptive real-time control scheme on the KSTAR tokamak as a viable approach to achieve an ELM-free state and simultaneously recover high-confinement (βN ~ 1.91, β p ~ 1.53, and H 98 ~ 0.9), demonstrating successful handling of a volatile complex system through adaptive measures. We show that, by exploiting a salient hysteresis process to adaptively minimize the RMP strength, stable ELM suppression can be achieved while actively encouraging confinement recovery. This is made possible by a self-organized transport response in the plasma edge which reinforces the confinement improvement through a widening of the ion temperature pedestal and promotes control stability, in contrast to the deteriorating effect on performance observed in standard RMP experiments. These results establish the real-time approach as an up-and-coming solution toward an optimized ELM-free state, which is an important step for the operation of ITER and reactor-grade tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO 2 interferometer on DIII-D

Abstract A Machine-Learning (ML) based detection scheme that automatically detects Alfvén Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the C O 2 phase data (simple and crosspower spectrograms). All C O 2 interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical C O 2 interferometer chord passing near the center is better for ML-based detection of AEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Insights on the cycling behavior of a highly-prelithiated silicon–graphite electrode in lithium-ion cells

Nanosized silicon materials are being developed for use in the anodes of high-energy lithium-ion batteries. However, the high surface areas of these materials increase the rate of parasitic reactions in the electrode, which consume cyclable Li + and degrade battery performance. Prelithiation offers a realistic strategy to compensate for this reactivity, by injecting additional charge into the cell to counterbalance the Li + loss. Interestingly, the benefits offered by prelithiation extend beyond its more obvious purpose. Here, by using a reference electrode in NMC532//Si–Gr cells, we show how prelithiation alters the cycling potentials experienced by the Si-containing anode and how that translates into gains in cycle life. The rate of consumption of the prelithiated charge is lower than that expected from the behavior of non-prelithiated cells. Curiously, the Si particles become partially unresponsive during the C/3 cycling apparently because of kinetic constraints. Electrochemical studies on harvested electrodes in half-cells show that capacities are intact after the long-term cycling and that most of the lithium reservoir is still present in the anode. We conclude that the high capacity retention displayed by the prelithiated cells mainly results from a higher participation of graphite particles during the extended electrochemical cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Source scaling comparison and validation in Central Italy: data intensive direct S waves versus the sparse data coda envelope methodology

SUMMARY Robustness of source parameter estimates is a fundamental issue in understanding the relationships between small and large events; however, it is difficult to assess how much of the variability of the source parameters can be attributed to the physical source characteristics or to the uncertainties of the methods and data used to estimate the values. In this study, we apply the coda method by Mayeda et al. using the coda calibration tool (CCT), a freely available Java-based code (https://github.com/LLNL/coda-calibration-tool) to obtain a regional calibration for Central Italy for estimating stable source parameters. We demonstrate the power of the coda technique in this region and show that it provides the same robustness in source parameter estimation as a data-driven methodology [generalized inversion technique (GIT)], but with much fewer calibration events and stations. The Central Italy region is ideal for both GIT and coda approaches as it is characterized by high-quality data, including recent well-recorded seismic sequences such as L'Aquila (2009) and Amatrice–Norcia–Visso (2016–2017). This allows us to apply data-driven methods such as GIT and coda-based methods that require few, but high-quality data. The data set for GIT analysis includes ∼5000 earthquakes and more than 600 stations, while for coda analysis we used a small subset of 39 events spanning 3.5 < Mw < 6.33 and 14 well-distributed broad-band stations. For the common calibration events, as well as an additional 247 events (∼1.7 < Mw < ∼5.0) not used in either calibration, we find excellent agreement between GIT-derived and CCT-derived source spectra. This confirms the ability of the coda approach to obtain stable source parameters even with few calibration events and stations. Even reducing the coda calibration data set by 75 per cent, we found no appreciable degradation in performance. This validation of the coda calibration approach over a broad range of event size demonstrates that this procedure, once extended to other regions, represents a powerful tool for future routine applications to homogeneously evaluate robust source parameters on a national scale. Furthermore, the coda calibration procedure can homogenize the Mw estimates for small and large events without the necessity of introducing any conversion scale between narrow-band measures such as local magnitude (ML) and Mw, which has been shown to introduce significant bias.

Morasca, Paola (ORCID:0000000265254867)↗
DOE OSTI2025

Theory of Two-Level Tunneling Systems in Superconductors

We develop a field theory formulation for the interaction of an ensemble of two-level tunneling systems (TLSs) with the electronic states of a superconductor. Predictions for the impact of two-level tunneling systems on superconductivity are presented, including T c and the spectrum of quasiparticle states for conventional BCS superconductors. We show that nonmagnetic TLS impurities in conventional s-wave superconductors can act as pair-breaking or pair-enhancing defects depending on the level population of the distribution of TLS impurities. We present calculations of the enhancement of superconductivity, both T c and the order parameter, for TLS defects in thermal equilibrium with the electrons and lattice. The scattering of quasiparticles by TLS impurities leads to subgap states below the bulk excitation gap, Δ, as well as resonances in the continuum above Δ. The energies and spectral weights of these states depend on the distribution of tunnel splittings, while the spectral weights are particularly sensitive to the level occupation of the TLS impurities. Under microwave excitation, or decoupling from the thermal bath, a nonequilibrium level population of the TLS distribution generates subgap quasiparticle states near the Fermi level that contribute to dissipation and thus degrade the performance of superconducting devices at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2022

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in-memory and near-memory architectures, as well as algorithmic approaches. In this report we leverage the low-power and the inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate neural network hardware inference based on passive arrays of MTJs. In general, transferring a trained network model to hardware for inference is confronted by degradation in performance due to device-to-device variations, write errors, parasitic resistance, and nonidealities in the substrate. To quantify the effect of these hardware realities, we benchmark 300 unique weight matrix solutions of a two-layer perceptron to classify the Wine dataset for both classification accuracy and write fidelity. Despite device imperfections, we achieve software-equivalent accuracy of up to 95.3% with proper tuning of network parameters in 15 x 15 MTJ arrays having a range of device sizes. The success of this tuning process shows that new metrics are needed to characterize the performance and quality of networks reproduced in mixed signal hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Experimental Characterization of Photoemission from Plasmonic Nanogroove Arrays

Metal photocathodes are an important source of high-brightness electron beams, ubiquitous in the operation of both large-scale accelerators and table-top microscopes. When the surface of a metal is nanoengineered with patterns on the order of the optical wavelength, it can lead to the excitation and confinement of surface-plasmon-polariton waves that drive nonlinear photoemission. In this work, we aim to evaluate gold plasmonic nanogrooves as a concept for producing bright electron beams for accelerators via nonlinear photoemission. Here, we do this by first comparing their optical properties to numerical calculations from first principles to confirm our ability to fabricate these nanoscale structures. Their nonlinear photoemission yield is found by measuring emitted photocurrent as the intensity of their driving laser is varied. Finally, the mean transverse energy of this electron source is found using the solenoid-scan technique. Our data demonstrate the ability of these cathodes to provide a tenfold enhancement in the efficiency of photoemission over flat metals driven with a linear process. We find that these cathodes are robust and capable of reaching sustained average currents over 100 nA at optical intensities larger than 2 GW cm –2 with no degradation of performance. The emittance of the generated beam is found to be highly asymmetric, a fact we can explain with calculations involving the also asymmetric roughness of the patterned surface. These results demonstrate the use of nanoengineered surfaces as enhanced photocathodes, providing a robust air-stable source of high-average-current electron beams hopefully with great potential for industrial and scientific applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Broadband coplanar-waveguide-based impedance-transformed Josephson parametric amplifier

Quantum-limited Josephson parametric amplifiers play a pivotal role in advancing the field of circuit quantum electrodynamics by enabling the fast and high-fidelity measurement of weak microwave signals. Therefore, it is necessary to develop robust parametric amplifiers with low noise, broad bandwidth, and reduced design complexity for microwave detection. However, current broadband parametric amplifiers either have degraded noise performance or rely on complex designs. Here, we present a device based on the broadband impedance-transformed Josephson parametric amplifier that integrates a hornlike coplanar waveguide transmission line, which significantly decreases the design and fabrication complexity while keeping comparable performance. The device shows an instantaneous bandwidth of 700 (200) MHz for 15 (20) dB gain with an average input saturation power of − 110 dBm and near quantum-limited added noise. The operating frequency can be tuned over 1.4 GHz using an external flux bias. We further demonstrate the negligible backaction from our device on a transmon qubit. The amplification performance and simplicity of our device promise its wide adaptation in quantum metrology, quantum communication, and quantum information processing. Published by the American Physical Society 2024

Qing, Bingcheng (ORCID:0000000345227017)↗
DOE OSTI2020

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F 1 score.

14 SOLAR ENERGY↗
DOE OSTI2022

Scaling Superconducting Quantum Computers with Chiplet Architectures

Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for meaningful demonstrations of quantum advantage requires overcoming many design challenges. For example, today’s transmon qubits suffer from significant variation due to limited precision in fabrication. As a result, barring significant improvements in current fabrication techniques, scaling QCs by building ever larger individual chips with more qubits is hampered by device variation. Severe device variation that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity. Our results demonstrate that chiplet architectures, relative to monolithic designs, benefit from average yield improvements ranging from 9.6 – 92.6 × for ≲5 qubit machines. In addition, our simulations explore the design space of chiplet systems and discover configurations that demonstrate average two-qubit gate infidelity reductions that are at best 0.815 × their monolithic counterpart. Lastly, we observe that carefully-selected modular systems achieve fidelity improvements on a range of benchmark circuits.

quantum architecture↗
DOE OSTI2020

Demonstrating the GaInP/GaAs Three-Terminal Heterojunction Bipolar Transistor Solar Cell

The three-terminal heterojunction bipolar transistor solar cell (HBTSC) concept enables the realization of a monolithic double-junction device with individual current extraction. We present an HBTSC realized by a heterojunction of GaInP and GaAs. The one-sun open-circuit voltage (V OC ) of the top and bottom junctions are 1.33 V and 0.99 V, respectively, while fill factors (FF) are above 80%. At one-sun illumination, reducing one junction's bias from V OC to maximum power point degrades the performance of the other junction only slightly (<; 0.5% efficiency loss). These results demonstrate the potential of the HBTSC concept to produce high-efficiency independently connected double-junction solar cells.

14 SOLAR ENERGY↗
DOE OSTI2023

Low-loss Si-based Dielectrics for High Frequency Components of Superconducting Detectors

Silicon-based dielectric is crucial for many superconducting devices, including high-frequency transmission lines, filters, and resonators. Defects and contaminants in the amorphous dielectric and at the interfaces between the dielectric and metal layers can cause microwave losses and degrade device performance. Optimization of the dielectric fabrication, device structure, and surface morphology can help mitigate this problem. We present the fabrication of silicon oxide and nitride thin film dielectrics. We then characterized them using Scanning Electron Microscopy, Atomic Force Microscopy, and spectrophotometry techniques. The samples were synthesized using various deposition methods, including Plasma-Enhanced Chemical Vapor Deposition and magnetron sputtering. The film's morphology and structure were modified by adjusting the deposition pressure and gas flow. The resulting films were used in superconducting resonant systems consisting of planar inductors and capacitors. Measurements of the resonator properties, including their quality factor, were performed.

low-loss dielectrics↗
DOE OSTI2025

Advanced Design for the WIQ Magnet With Steering Corrector Function

The Facility for Rare Isotopes Beams (FRIB) delivers heavy-ion primary beams at energies of up to 300 MeV/u at 10 kW of beam power to generate rare isotope beams for experiments and will eventually operate at beam power of 400 kW. The preseprator of the Advanced Rare Isotope Separator (ARIS) is equipped with six warm-iron quadrupole (WIQ) singlets and two dipoles integrated right after the production target. They have a compact structure and operate in a high radiation vacuum environment within a hot cell having remote handling capabilities for installation and maintenance. Due to asymmetry with respect to the quadrupole poles, nested sextupole excitations in WIQs induce vertical dipoles that offset the centroid trajectory; Magnet misalignments also result in trajectory offsets. Such offsets degrade separator performance but can be minimized by changing the current distribution on sextupole and octupole coils. In this work, we show how modifications to the WIQ coil design can allow superimposed dipole fields to be included to the octupole and sextupole windings, as well as addition of dipole components by splitting coil currents over groups with separator power supplies. Adjusting the group currents can cancel the sextupole-induced vertical dipole component which can be as high as 0.012 Tm. Octupole coil changes may superimpose a horizontal dipole integrated strength as high as 0.0332 Tm. Unwanted higher harmonics induced as a side effect of the new design are kept to a minimum such that separator performance is preserved as much as possible.

Accelerator magnets↗
DOE OSTI2021

Response of Integrated Silicon Microwave pin Diodes to X-ray and Fast-Neutron Irradiation

Here, integrated silicon microwave pin diodes are exposed to 10-keV X-rays up to a dose of 2 Mrad(SiO 2 ) and 14-MeV fast neutrons up to a fluence of 2.2×10 13 cm -2 . Changes in both DC leakage current and small-signal circuit components are examined. Degradation in performance due to total-ionizing dose is shown to be suppressed by non-quasi-static effects during RF operation. Tolerance to displacement damage from fast neutrons is also observed, which is explained using TCAD simulations. Overall, the characterized pin diodes are tolerant to cumulative radiation at levels consistent with space applications such as geosynchronous weather satellites.

42 ENGINEERING↗