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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Observation of well-defined Kohn-anomaly in high-quality graphene devices at room temperature

Abstract Due to its ultra-thin nature, the study of graphene quantum optoelectronics, like gate-dependent graphene Raman properties, is obscured by interactions with substrates and surroundings. For instance, the use of doped silicon with a capping thermal oxide layer limited the observation to low temperatures of a well-defined Kohn-anomaly behavior, related to the breakdown of the adiabatic Born–Oppenheimer approximation. Here, we design an optoelectronic device consisting of single-layer graphene electrically contacted with thin graphite leads, seated on an atomically flat hexagonal boron nitride substrate and gated with an ultra-thin gold layer. We show that this device is optically transparent, has no background optical peaks and photoluminescence from the device components, and no generation of laser-induced electrostatic doping (photodoping). This allows for room-temperature gate-dependent Raman spectroscopy effects that have only been observed at cryogenic temperatures so far, above all the Kohn-anomaly phonon energy normalization. The new device architecture, by decoupling graphene optoelectronic properties from the substrate effects, allows for observing quantum phenomena at room temperature.

Materials Science↗
DOE OSTI2023

Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices

Abstract Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole–Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland–Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.

Liu, Fangyu (ORCID:0000000315541478)↗
DOE OSTI2021

Radon Kinetics in a Basement Space Measured with Different Devices

Although indoor monitoring of radon and benchmarking of radon measurement devices remain important research topics, few intercomparisons of active radon measurement devices have been performed under realistic conditions, let alone dynamic ones enabling comparison of their transient behavior. Five different radon monitors were therefore placed in a poorly ventilated basement space under three different conditions: 24 h under a steady, elevated radon level, 24 h with fans turned on to produce a radon washout transient, and 9 d with fans turned off for a radon buildup transient. Resulting radon concentrations varied between ~200 and ~2,000 Bq m -3 . Accuracy of the devices were evaluated using root-mean-square error, and ventilation data were fit to first order linear compartmental models. To more accurately model behaviors such as cyclic diurnal variations, the source term corresponding to entry of radon from soil into the basement was considered to be non-constant, as it is likely to vary drastically with both the indoor-outdoor pressure differential and soil concentration variations. Here, the improved radon washout model fit very well with the measurements. Despite a wide variety in list prices, all devices performed similarly during transients and at different radon concentrations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Autotuning of Double-Dot Devices In Situ with Machine Learning

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. Here, we demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Toward Robust Autotuning of Noisy Quantum dot Devices

The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a “gatekeeper” system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9)% when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Spin-helical detection in a semiconductor quantum device with ferromagnetic contacts

Spin-helical states, which arise in quasi-one-dimensional (1D) channels with spin-orbital (SO) coupling, underpin efforts to realize topologically protected quantum bits based on Majorana modes in semiconductor nanowires. Detecting helical states is challenging due to non-idealities present in real devices. Past efforts have focused on devices with normal contacts. In contrast, here we show by means of tight-binding calculations that by using ferromagnetic contacts it is possible to detect helical modes with high sensitivity even in the presence of realistic device effects, such as quantum interference. We show that by taking advantage of the spin-selective transmission properties of helical states, new transport signatures become possible. In addition, we show that spin-polarized contacts provide a unique path to investigate the spin-texture and spin-momentum locking properties of helical states. Our results are of interest not only for the ongoing development of Majorana qubits, but also for realizing possible spin-based quantum devices, such as quantum spin modulators and interconnects based on spin-helical channels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dark Matter Induced Power in Quantum Devices

We point out that power measurements of single quasiparticle devices open a new avenue to detect dark matter (DM). The threshold of these devices is set by the Cooper pair binding energy, and is therefore so low that they can detect DM as light as about an MeV incoming from the Galactic halo, as well as the low-velocity thermalized DM component potentially present in the Earth. Using existing power measurements with these new devices, as well as power measurements with SuperCDMS-CPD, we set new constraints on the spin-independent DM scattering cross section for DM masses from about 10 MeV to 10 GeV. We outline future directions to improve sensitivity to both halo DM and a thermalized DM population in the Earth using power deposition in quantum devices. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

From Design to Device: Challenges and Opportunities in Computational Discovery of p -Type Transparent Conductors

A high-performance p -type transparent conductor (TC) does not yet exist but could lead to advances in a wide range of optoelectronic applications and enable new architectures for, e.g., next-generation photovoltaic (PV) devices. High-throughput computational material screenings have been a promising approach to filter databases and identify new p -type TC candidates and some of these predictions have been experimentally validated. However, most of these predicted candidates do not have experimentally achieved properties on par with n -type TCs used in solar cells and therefore have not yet been used in commercial devices. Thus, there is still a significant divide between transforming predictions into results that are actually achievable in the laboratory and an even greater lag in scaling predicted materials into functional devices. In this perspective, we outline some of the major disconnects in this materials discovery process—from scaling computational predictions into synthesizable crystals and thin films in the laboratory to scaling laboratory-grown films into real-world solar devices—and share insights to inform future strategies for TC discovery and design. Published by the American Physical Society 2024

14 SOLAR ENERGY↗
DOE OSTI2021

Impact of Random Spatial Fluctuation in Non-Uniform Crystalline Phases on the Device Variation of Ferroelectric FET

In this work, a comprehensive study of random spatial fluctuation of the ferroelectric (FE) phase and dielectric (DE) phase in FeFETs is conducted to understand its impact on device variation. It is found that: i) there exists a certain DE percentage threshold that below which the increase of the DE phase does not significantly impact the device memory window and variation and only above which evident device degradation can be observed; ii) increasing the DE phase increases the variation in the memory window and the coercive field distribution further exacerbates the variation, hence degrading the sensing margin; iii) decreasing the number of grains degrades the device variation, which calls for further grain size engineering for variation suppression.

42 ENGINEERING↗
DOE OSTI2024

Transformer Masked Autoencoders for RF Device Fingerprinting

Machine learning methods for RF device fingerprinting typically rely on CNN-based models. Transformer-based models have outperformed CNNs for modulation classification tasks, but there are few implementations for device fingerprinting. We train a transformer for device fingerprinting with the largest device count to date and explore several variations of the architecture. Additionally, we demonstrate that pre-training an RF transformer as a Masked Autoencoder improves classification accuracy, as has been observed for CNN fingerprinting models and vision transformers.

artificial intelligence↗
DOE OSTI2021

(110)-Oriented GaAs Devices and Spalling as a Platform for Low-Cost III-V Photovoltaics

We demonstrate the growth of (110) GaAs solar cells by hydride vapor phase epitaxy (HVPE) as an advance towards a (110)-oriented device platform with substrate reuse via spalling. Controlled spalling offers a fracture-based path to substrate amortization, allowing device removal and substrate reuse, but the faceted surface generated in spalling of (100)-GaAs presents hurdles to direct regrowth of subsequent devices. Spalling of (110)-oriented substrates takes advantage of the natural (110) cleavage plane in zinc-blende III-V materials, eliminating this faceting. The vast majority of III-V epitaxy development is on (100)-oriented substrates, but epitaxy is not limited to this orientation. Here, we demonstrate (110)-based GaAs solar cells grown by HVPE with equivalent performance to devices grown on the more standard (100) orientation. We also demonstrate spalling of (110) oriented substrates, revealing surfaces with flat terraces separated by steps. These results provide preliminary evidence of a potentially low-cost path for terrestrial III-V photovoltaics via the (110) substrate orientation.

(110) orientation↗
DOE OSTI2020

Arsenic Doping of Polycrystalline CdSeTe Devices for Microsecond Life-times with High Carrier Concentrations

We report seminal advances in fabrication and understanding of group V (As) doped thin-film polycrystalline CdTe-based solar cells. The devices are fabricated using a novel approach, by sublimating layers of CdSeTe and CdSeTe:As. This new method allowed us to achieve minority carrier lifetime of over 1 μs, carrier concentration of more than 5×l0 15 cc -1 and external radiative efficiency of over 2 % in a device configuration. We find an increase in open-circuit voltage when comparing As-doped, Cu-doped and undoped devices. The choice of CdSeTe instead of a CdTe-only absorber has been explained using first-principle density functional theory model. A SCAPS device model is used to analyze the potential causes for lower open-circuit voltage.

Munshi, Amit H.↗
DOE OSTI2022

Na Diffusion and Device Performance of AgBr Treated CuGaSe 2 Thin Films

Previous work demonstrated that uniform CuGaSe2 (CGS) thin films with large grains could be grown using a short AgBr vapor treatment during growth. Here, devices made with this treated CGS showed better performance compared to devices made with standard material. Here, it is shown that AgBr treated CGS device performance worsens over time and is attributed to the suppression of Na diffusion. A NaF post-deposition treatment is shown to effectively introduce Na into the AgBr treated CGS film and prevent device degradation.

14 SOLAR ENERGY↗
DOE OSTI2020

Characterizing the Stability of NISQ Devices

In this study, we focus on the question of stability of NISQ devices. The parameters that define the device stability profile are motivated by the work of DiVincenzo in [9] where the requirements for physical implementation of quantum computing are discussed. We develop the metrics and theoretical framework to quantify the DiVincenzo requirements and study the stability of those key metrics. The basis of our assessment is histogram similarity (in time and space). For identical experiments, devices which produce reproducible histograms in time, and similar histograms in space, are considered more reliable. To investigate such reliability concerns robustly, we propose a moment-based distance (MBD) metric. We illustrate our methodology using data collected from IBM's Yorktown device. Two types of assessments are discussed: spatial stability and temporal stability.

Dasgupta, Samudra↗
DOE OSTI2022

Adaptive stabilization of quantum circuits executed on unstable devices

Conventional computers have evolved to device components that demonstrate failure rates of 10 −17 or less, while current quantum computing devices typically exhibit error rates of 10 −2 or greater. This raises concerns about the reliability and reproducibility of the results obtained from quantum computers. The problem is highlighted by experimental observation that today’s NISQ devices are inherently unstable. Remote quantum cloud servers typically do not provide users with an ability to calibrate the device themselves. Using inaccurate characterization data for error mitigation can have devastating impact on reproducibility. In this study, we investigate if one can infer the critical channel parameters dynamically from the noisy binary output of the executed quantum circuit and use it to improve program stability. An open question however is how well does this methodology scale. We discuss the efficacy and efficiency of our adaptive algorithm using canonical quantum circuits such as the uniform superposition circuit. Our metric of performance is the Hellinger distance between the post-stabilization observations and the reference (ideal) distribution.

Dasgupta, Samudra↗
DOE OSTI2023

Reliable Devices Yield Stable Quantum Computations

Stable quantum computation requires noisy results to remain bounded even in the presence of noise fluctuations. Yet non-stationary noise processes lead to drift in the varying characteristics of a quantum device that can greatly influence the circuit outcomes. Here we address how temporal and spatial variations in noise relate device reliability to quantum computing stability. First, our approach quantifies the differences in statistical distributions of characterization metrics collected at different times and locations using Hellinger distance. We then validate an analytical bound that relates this distance directly to the stability of a computed expectation value. Our demonstration uses numerical simulations with models informed by the washington superconducting transmon device. We find that the stability metric is consistently bounded from above by the corresponding Hellinger distance, which can be cast as a specified tolerance level. These results underscore the significance of reliable quantum computing devices and the impact for stable quantum computation.

Dasgupta, Samudra↗
DOE OSTI2023

Data for Construction of a Compact Array of Microplasma Jet Devices and Its Application for Random Mutagenesis of Rhodosporidium toruloides

A small and efficient DNA mutation-inducing machine was constructed with an array of microplasma jet devices (7 × 1) that can be operated at atmospheric pressure for microbial mutagenesis. Using this machine, we report disruption of a plasmid DNA and generation of mutants of an oleaginous yeast Rhodosporidium toruloides . Specifically, a compact-sized microplasma channel (25 × 20 × 2 mm3) capable of generating an electron density of greater than 1013 cm–3 was constructed to produce reactive species (N2*, N2+, O, OH, and Hα) under helium atmospheric conditions to induce DNA mutagenesis. The length of microplasma channels in the device played a critical role in augmenting both the volume of plasma and the concentration of reactive species. First, we confirmed that microplasma treatment can linearize a plasmid by creating nicks in vitro. Second, we treated R. toruloides cells with a jet device containing 7 microchannels for 5 min; 94.8% of the treated cells were killed, and 0.44% of surviving cells showed different colony colors as compared to their parental colony. Microplasma-based DNA mutation is energy-efficient and can be a safe alternative for inducing mutations compared to conventional methods using toxic mutagens. This compact and scalable device is amenable for industrial strain improvement involving large-scale mutagenesis.

Conversion↗
DOE OSTI2023

Fabrication-robust silicon photonic devices in standard sub-micron silicon-on-insulator processes

Perturbations to the effective refractive index from nanometer-scale fabrication variations in waveguide geometry plague high index-contrast photonic platforms; this includes the ubiquitous sub-micron silicon-on-insulator (SOI) process. Such variations are particularly troublesome for phase-sensitive devices, such as interferometers and resonators, which exhibit drastic changes in performance as a result of these fabrication-induced phase errors. In this Letter, we propose and experimentally demonstrate a design methodology for dramatically reducing device sensitivity to silicon width variations. We apply this methodology to a highly phase-sensitive device, the ring-assisted Mach–Zehnder interferometer (RAMZI), and show comparable performance and footprint to state-of-the-art devices, while substantially reducing stochastic phase errors from etch variations. This decrease in sensitivity is directly realized as energy savings by significantly reducing the required corrective thermal tuning power, providing a promising path toward ultra-energy-efficient large-scale silicon photonic circuits.

Rizzo, Anthony (ORCID:000000034752797X)↗