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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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Statistical reduction of satellite effective radio cross section of reflected radio signal power measurements made on Echo I passive satellite
Lunar radar echoes depolarization studied via lunar surface backscattering characteristics at 23 cm wavelength
Fluorine-19 ( 19 F) magnetic resonance imaging (MRI) is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes three-dimensional spin-density–weighted 19 F imaging challenging. Recent advances in tracer paramagnetic metallo-perfluorocarbon (MPFC) nanoemulsion probes have shown multifold SNR improvements due to an accelerated 19 F T 1 relaxation rate and a commensurate gain in imaging speed and averages. However, 19 F T 2 -reduction and increased linewidth limit the amount of metal additive in MPFC probes, thus constraining the ultimate SNR. To overcome these barriers, we describe a compressed sampling (CS) scheme, implemented using a “zero” echo time (ZTE) sequence, with data reconstructed via a sparsity-promoting algorithm. Our CS-ZTE scheme acquires k-space data using an undersampled spherical radial pattern and signal averaging. Image reconstruction employs off-the-shelf sparse solvers to solve a joint total variation and -norm regularized least square problem. To evaluate CS-ZTE, we performed simulations and acquired 19 F MRI data at 11.7 T in phantoms and mice receiving MPFC-labeled dendritic cells. For MPFC-labeled cells in vivo, we show SNR gains of ~6.3 × with 8-fold undersampling. We show that this enhancement is due to three mechanisms including undersampling and commensurate increase in signal averaging in a fixed scan time, denoising attributes from the CS algorithm, and paramagnetic reduction of T 1 . Importantly, 19 F image intensity analyses yield accurate estimates of absolute quantification of 19 F spins. Overall, the CS-ZTE method using MPFC probes achieves ultrafast imaging, a substantial boost in detection sensitivity, accurate 19 F spin quantification, and minimal image artifacts.
The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.
Nonlinear acoustic methods demonstrate high sensitivity for concrete damage evaluation. Among these methods, nonlinear resonance acoustic methods have been widely used in laboratory tests on small scale test samples. In this study, a nonlinear impact-echo (IE) method for concrete damage evaluation was introduced, in which the IE frequency shift was measured at different impact force levels. The nonlinear IE test and the nonlinear impact resonance acoustic spectroscopy (NIRAS) test share similarities in the experimental setup and analysis method. However, the nonlinear IE test excites a local thickness resonance mode of a member instead of the global resonance vibration. Therefore, the analyzed mode is unaffected by member boundary conditions and is applicable to large concrete structural members. To identify the fundamental IE frequencies, multiple impacts were applied along the members. Once the fundamental IE frequencies were determined, the effectiveness of the nonlinear IE method was evaluated by testing seven concrete beam specimens with varying levels of alkali-silica reaction (ASR) damage. In conclusion, the nonlinear IE test demonstrates high sensitivity to concrete damage and allows for quantitative assessment of the damage state of concrete without a baseline measurement.
High-resolution neutron spin echo (NSE) spectroscopy offers unique insights into the mobility of molecular (sub)structures excited by thermal fluctuations (i.e., Brownian motion) of “soft matter” as polymers in solution and in the melt, in biological matter (e.g., protein motions), membranes, and glasses. The ability to tag substructures using H, D contrast variation to resolve the relevant timescales of dynamics on selected molecular items in liquids, soft matter, and melts is a significant advantage of NSE. Also slow magnetic fluctuations on molecular length scales in the range of nanoseconds, e.g., in spin glasses or topological spin structures, can be accessed. This paper reviews the highlights and peculiarities of the SNS-NSE, based at the pulsed neutron source SNS, during its first 1.5 decades of operation. An outlook and perspectives of research in the domain of high-resolution spectroscopy is given.
As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.
Abstract The relaxation behavior of glass formers exhibits spatial heterogeneity and dramatically changes upon cooling towards the glass transition. However, the underlying mechanisms of the dynamics at different microscopic length scales are not fully understood. Employing the recently developed wide-angle neutron spin-echo spectroscopy technique, we measured the Q -dependent coherent intermediate scattering function of a prototypical ionic glass former Ca 0.4 K 0.6 (NO 3 ) 1.4 , in the highly viscous liquid state. In contrast to the structure modulated dynamics for Q < 2.4 Å −1 , i.e., at and below the structure factor main peak, for Q > 2.4 Å −1 , beyond the first minimum above the structure factor main peak, the stretching exponent exhibits no temperature dependence and concomitantly the relaxation time shows smaller deviations from Arrhenius behavior. This finding indicates a change in the dominant relaxation mechanisms around a characteristic length of 2π/(2.4 Å −1 ) ≈ 2.6 Å, below which the relaxation process exhibits a temperature independent distribution and more Arrhenius-like behavior.
Abstract Inhomogeneities crucially influence the properties of quantum materials, yet methods that can measure them remain limited and can access only a fraction of relevant observables. For example, local probes such as scanning tunnelling microscopy have documented that the electronic properties of cuprate superconductors are inhomogeneous over nanometre length scales. However, complementary techniques that can resolve higher-order correlations are needed to elucidate the nature of these inhomogeneities. Furthermore, local tunnelling probes are often effective only far below the critical temperature. Here we develop a two-dimensional terahertz spectroscopy method to measure Josephson plasmon echoes from an interlayer superconducting tunnelling resonance in a near-optimally doped cuprate. The technique allows us to study the multidimensional optical response of the interlayer Josephson coupling in the material and disentangle intrinsic lifetime broadening from extrinsic inhomogeneous broadening for interlayer superconducting tunnelling. We find that inhomogeneous broadening persists up to a substantial fraction of the critical temperature, above which this is overcome by the thermally increased lifetime broadening.
Electronic spins of nitrogen–vacancy centres in diamond constitute a promising system for micro- and nanoscale magnetic sensing, because of their operation under ambient conditions, ease of placement in close proximity to sensing targets and biological compatibility. At high densities, the electronic spins interact through dipolar coupling, which typically limits but can also potentially enhance sensing performance. Here we report the experimental demonstration of many-body signal amplification in a solid-state, room-temperature quantum sensor. Our approach uses time-reversed two-axis-twisting interactions, engineered through dynamical control of the quantization axis and Floquet engineering in a two-dimensional ensemble of nitrogen–vacancy centres. We observe that optimal amplification occurs when the backward evolution time equals twice the forward evolution time, in sharp contrast to the conventional Loschmidt echo. These observations can be understood as resulting from an underlying time-reversed mirror symmetry of the microscopic dynamics, providing key insights into signal amplification and opportunities for practical entanglement-enhanced quantum sensing.
Prebunching via echo-enabled harmonic generation (EEHG) is an efficient way to reduce the radiator length and improve the longitudinal coherence as well as output stability in storage-ring-based free-electron lasers. We propose a conceptual design, which uses two straight sections to seed coherent extreme-ultraviolet (EUV) and soft X-ray emission with nearly MHz repetition rate. To take the large energy spread (10 –3 ) of a storage ring into account and utilize the existing bending magnets between the two straight sections as the first chicane, we implement a special modeling tool, named EEHG optimizer. This tool has been successfully applied to maximize the prebunching with a reasonably low energy modulation, thereby generating intense coherent X-ray pulses within a short undulator length (a few meters) limited by the available space of a storage ring. Numerical simulations confirm that the optimized EEHG parameters can be directly applied to generate a 10 MW scale peak power with fully coherent ultrafast EUV to soft X-ray pulses based on the NSLS-II parameters. This method can be easily extended to other types of diffraction-limited storage rings.
Neutron spin echo (NSE) spectroscopy is a powerful technique used to probe the internal dynamics and diffusion processes within matter, allowing researchers to quantify how materials respond to changes in external conditions. To fully understand the bulk properties of a material, it is often crucial to investigate dynamic processes occurring on nanometre length scales and nanosecond timescales. NSE spectroscopy provides an exceptional capability to measure such dynamics across a broad spectrum of condensed matter systems, ranging from proteins and quantum magnets to catalysts. In this Primer, we present an overview of NSE spectroscopy, highlighting the instruments used, methods of data collection and representative applications in both soft-matter and hard-matter sciences.
Having previously reported on bunching via echo-enabled harmonic generation (EEHG) as an effective way to improve the longitudinal coherence in the NSLS-II storage ring [X. Yang et al., Sci. Rep. 12, 9437 (2022)], we demonstrate that this EEHG scheme can be easily adopted to any fourth generation diffraction-limited synchrotron light source with significant benefits. The advantage of the scheme is that it requires no change of the lattice and is fully compatible with other beamlines. Since the EEHG performance is mainly determined by the momentum compaction, beam emittances, and beta functions of a SR lattice, we have identified these crucial parameters and successfully built a generalized model, which can predict the performance of nearly any SLS. Regarding the fourth generation SLSs, momentum compactions are often significantly smaller; thus, to cover the x rays with a photon energy of up to 1 keV, we utilize a specific design, including a 250 nm seed-laser wavelength. Our model predicts that for most of the current and future fourth generation SLSs, the EEHG scheme can produce significant prebunching up to harmonic 200 and, thus, generate a few MW scale peak power at 1.25 nm wavelength.
We study the cosmological propagation of gravitational waves (GWs) beyond general relativity (GR) across homogeneous and isotropic backgrounds. We consider scenarios in which GWs interact with an additional tensor field and use a parametrized phenomenological approach that generically describes their coupled equations of motion. We analyze four distinct classes of derivative and non-derivative interactions: mass, friction, velocity, and chiral. We apply the WKB formalism to account for the cosmological evolution and obtain analytical solutions to these equations. We corroborate these results by analyzing numerically the propagation of a toy GW signal. We then proceed to use the analytical results to study the modified propagation of realistic GWs from merging compact binaries, assuming that the GW signal emitted is the same as in GR. We generically find that tensor interactions lead to copies of the originally emitted GW signal, each one with its own possibly modified dispersion relation. These copies can travel coherently and interfere with each other leading to a scrambled GW signal, or propagate decoherently and lead to echoes arriving at different times at the observer that could be misidentified as independent GW events. Depending on the type of tensor interaction, the detected GW signal may exhibit amplitude and phase distortions with respect to a GW waveform in GR, as well as birefringence effects. Furthermore, we discuss observational probes of these tensor interactions with both individual GW events, as well as population studies for both ground- and space-based detectors.
The Krylov subspace method is a traditional approach to approximate quantum evolution, allowing us to treat systems with large Hilbert spaces. Despite its popularity, current bounds typically overestimate the error, which translates into more expensive simulation routines. Here, in this paper, we tackle this problem by realizing that the error can be understood as a Loschmidt echo in a one-dimensional (1D) noninteracting tight-binding Hamiltonian. We show that the different time regimes of the approximation can be understood using simple physical ideas. More importantly, we obtain computationally cheap error bounds that describe with high precision the actual error in the approximation.
We report the hybridization of magnons and phonons is playing a critical role in the emerging field of hybrid magnonics because it combines the high tunability of magnetism with the long lifetime of mechanics for comprehensive coherent information processing. Recently there has been increasing interest in thin-film bulk acoustic waves because of their long lifetimes at high frequencies. However, the unique multimode nature of such phonon modes has not been exploited as an important resource for coherent information processing. In this Letter we study the simultaneous hybridization of multiple high-overtone bulk acoustic resonances with a magnon and a microwave mode. The demonstrated multimode hybridization allows us to observe coherent pulse echoes, opening opportunities for both fundamental studies and practical applications of hybrid magnonics.
An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Furthermore, our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.
The use of superconducting microresonators together with quantum-limited Josephson parametric amplifiers has enhanced the sensitivity of pulsed electron spin resonance (ESR) measurements by more than four orders of magnitude. So far, the microwave resonators and amplifiers have been designed as separate components due to the incompatibility of Josephson junction–based devices with magnetic fields. This has produced complex spectrometers and raised technical barriers toward adoption of the technique. Here, we circumvent this issue by coupling an ensemble of spins directly to a weakly nonlinear and magnetic field–resilient superconducting microwave resonator. We perform pulsed ESR measurements with a 1-pL mode volume containing 6 × 10 7 spins and amplify the resulting signals within the device. When considering only those spins that contribute to the detected signals, we find a sensitivity of 2.8 x 10 3 spins / $\sqrt{Hz}$ for a Hahn echo sequence at a temperature of 400 mK. In situ amplification is demonstrated at fields up to 254 mT, highlighting the technique’s potential for application under conventional ESR operating conditions.