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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

NMR study of Ni 50+x ⁢Ti 50-x strain glasses

Here, we studied Ni 50+x ⁢Ti 50-x with compositions up to x = 2, performing 47 Ti and 49 Ti nuclear magnetic resonance (NMR) measurements from 4 to 400 K. For large x in this system, a strain glass appears in which frozen ferroelastic nanodomains replace the displacive martensite structural transition. Here, we demonstrate that NMR can provide an extremely effective probe of the strain-glass freezing process, with large changes in NMR line shape due to the effects of random strains which become motionally narrowed at high temperatures. At the same time with high-resolution x-ray diffraction we confirm the lack of structural changes in x ≥ 1.2 samples, while we show that there is little change in the electronic behavior across the strain-glass freezing temperature. NMR spin-lattice relaxation time (T 1 ) measurements provide a further measure of the dynamics of the freezing process, and indicate a predominantly thermally activated behavior both above and below the strain-glass freezing temperature. We show that the strain-glass results are consistent with a very small density of critically divergent domains undergoing a Vogel-Fulcher-type freezing process, coexisting with domains exhibiting faster dynamics and stronger pinning.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1975

The effect of adiabatic focusing upon charged particle propagation in random magnetic fields

This paper describes the effects of strong adiabatic focusing in terms of eigenfunctions of an operator which incorporates both scattering by random fields and focusing by a spatially inhomogeneous guiding field. It is found that focused transport differs from rectilinear transport in the following ways: (1) the coherent velocity toward stronger fields is larger than that toward weaker fields; (2) coherent effects are more prominent relative to diffusive effects when focusing is present; and (3) initial anisotropies at injection have a minimal influence upon rectilinear transport but they have a pronounced effect on focused transport. These features explain certain well-known discrepancies between the diffusive picture and the observed profiles of solar particle events. In particular, they explain the so-called scatter-free propagation of kilovolt flare electrons.

Earl, J. A.↗
DOE OSTI2024

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.

high entropy oxides↗
DOE OSTI2025

Observable measurement-induced transitions

One of the main postulates of quantum mechanics is that measurements destroy quantum coherence (wave function collapse). Recently it was discovered that in a many-body system dilute local measurements still preserve some coherence across the entire system. As the measurement density is increased, a phase transition occurs that is characterized by the disentanglement of different parts of the system. Unfortunately, this transition is impossible to observe experimentally for macroscopic systems because it requires an exponentially costly full tomography of the many-body wave function or a comparison with the simulation on an oracle classical computer. In this work we report the discovery of another measurement-induced phase transition that can be observed experimentally if quantum dynamics can be reversed. On one side of this phase transition the quantum information encoded in some part of the Hilbert space is fully recovered after the time inversion. On the other side, all quantum information is corrupted. This transition also manifests itself as the change in the behavior of the probability to observe the same measurement outcome in the process that consists of identical blocks repeated many times. In each block the unitary evolution is followed by the measurement. On one side of the transition the probability decreases exponentially with the number of repetitions, on the other it tends to a constant as the number of repetitions is increased. We confirm the existence of the proposed phase transition through numerical simulations of realistic quantum circuits and analytical calculations using an effective random-matrix theory model.

Measurement-induced phase transitions↗
DOE OSTI2022

Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights

The electrochemical nitrate reduction reaction (NO 3 RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO 3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO 3 RR to ammonia with a Faradaic efficiency of 92.5% at –0.5 V RHE and a yield rate of 6.25 mol h –1 g –1 at –0.6 V RHE . This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Numerical simulation of the beam self-cleaning process in a multimode graded-index fibre during propagation of a pump wave and a Stokes component

A model of coupled modes is proposed, which makes it possible to describe the propagation of a pump wave and a Stokes component in a multimode graded-index fibre. A decrease in the depression in the centre of the profile of the pump wave intensity distribution as a result of the influence of Kerr effects and random linear coupling of modes is demonstrated. (paper)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Production efficiencies of sound waves in the intracluster medium driven by AGN jets

Feedback from active galactic nuclei (AGNs) is believed to be the most promising solution to the cooling flow problem in cool-core clusters. Dissipation of sound waves is considered as one of the possible heating mechanisms; however, its relative contribution to heating remains unclear. To estimate the energy budget for heating, we perform 3D hydrodynamic simulations of AGN jet injections in a Perseus-like cluster and quantify the amount of energy stored in the forms of weak shocks and waves. For this work, e find that, for a single jet injection with typical parameters in cool-core clusters, ${\sim}9{{\ \rm per\ cent}}$ of the total jet energy is stored in compressional waves (including both shocks and waves). However, due to the destructive effects among randomly phased waves as well as the dissipation of shock energies, in our simulations including self-regulated AGN feedback, no more than $3{{\ \rm per\ cent}}$ of the total injected energy goes into compressional waves. We further separate the energy contribution from shocks and waves and find that, for a single outburst, the shocks can only contribute to ${\sim}20{-}30{{\ \rm per\ cent}}$ of the total compressional energy in the inner radii and quickly dissipate away. In the self-regulated case where shocks are repeatedly generated, shocks completely dominate over sound waves in the inner region and can still provide ${\sim}40{-}50{{\ \rm per\ cent}}$ of the total compressional energy at outer radii. Our results suggest that the production of sound waves is not as efficient as what was previously found, and thus sound wave dissipation may be a subdominant source of heating in cool-core clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Charge density wave and superconductivity in the disordered Holstein model

The interplay between electron-electron correlations and disorder has been a central theme of condensed matter physics over the past several decades, with particular interest in the possibility that interactions might cause delocalization of an Anderson insulator into a metallic state, and the disrupting effects of randomness on magnetic order and the Mott phase. In this work, we extend this physics to explore electron-phonon interactions and show, via exact quantum Monte Carlo simulations, that the suppression of the charge density wave correlations in the half-filled Holstein model by disorder can stabilize a superconducting phase. Our simulations thus capture qualitatively the suppression of charge ordered phases and emergent superconductivity recently seen experimentally.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Higher-order topological corner and bond-localized modes in magnonic insulators

We theoretically investigate a two-dimensional decorated honeycomb lattice framework to realize a second-order topological magnon insulator (SOTMI) phase featuring distinct corner-localized modes. Here, our study emphasizes the pivotal role of spin-magnon mapping in characterizing bosonic topological properties, which exhibit differences from their fermionic counterparts. We employ a symmetry indicator topological invariant to identify and characterize this SOTMI phase, particularly for systems respecting time-reversal and rotational symmetry. Using a spin model defined on a honeycomb lattice geometry, we demonstrate that introducing “kekulé” type distortions yields a topological phase. In contrast, “antikekulé” distortions result in a nontopological magnonic phase. The presence of kekulé distortions manifests in two distinct topologically protected bosonic corner modes—an intrinsic and a pseudo, based on the specific edge terminations. On the other hand, antikekulé distortions give rise to Tamm/Shockley type bond-localized boundary modes, which are nontopological and reliant on particular edge termination. We further investigate the effects of random out-of-plane exchange anisotropy disorder on the robustness of these bosonic corner modes. The distinction between SOTMIs and their fermionic counterparts arises due to the system-specific magnonic onsite energies, a crucial feature often overlooked in prior literature. Our study unveils exciting prospects for engineering higher-order topological phases in magnon systems and enhances our understanding of their unique behavior within decorated honeycomb lattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Scattering of Magnons at Graphene Quantum-Hall-Magnet Junctions

Motivated by recent nonlocal transport studies of quantum-Hall-magnet (QHM) states formed in monolayer graphene’s N = 0 Landau level, here we study the scattering of QHM magnons by gate-controlled junctions between states with different integer filling factors ν . For the ν = 1 | – 1 | 1 geometry we find that magnons are weakly scattered by electric potential variation in the junction region, and that the scattering is chiral when the junction lacks a mirror symmetry. For the ν = 1 | 0 | 1 geometry, we find that kinematic constraints completely block magnon transmission if the incident angle exceeds a critical value. Our results explain the suppressed nonlocal–voltage signals observed in the ν = 1 | 0 | 1 case. We use our theory to propose that valley waves generated at ν = – 1 | 1 junctions and magnons can be used in combination to probe the spin or valley flavor structure of QHM states at integer and fractional filling factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Barriers to carrier transport in multiple quantum well nitride-based c -plane green light emitting diodes

The presence of alloy disorder in III-nitride materials has been demonstrated to play a significant role in device performance through effects such as carrier localization and carrier transport. Relative to blue light emitting diodes (LEDs), these effects become more severe at green wavelengths. Because of the potential fluctuations that arise due to alloy disorder, full three-dimensional (3D) simulations are necessary to accurately relate materials properties to device performance. We demonstrate experimentally and through simulation that increased quantum well (QW) number in c-plane green LEDs contributes to excess driving voltage, and therefore reduced electrical efficiency. Experimentally, we grew an LED series with the number of QWs varying from one to seven and observed a systematic increase in voltage with the addition of each QW. Trends in LED electrical properties obtained from 3D simulations, which account for the effects of random alloy fluctuations, are in agreement with experimental data. Agreement is achieved without the need for adjusting polarization parameters from their known values. Finally, from these results, we propose that the polarization induced barriers at the GaN/InGaN (lower barrier/QW) interfaces and the sequential filling of QWs both contribute significantly to the excess forward voltage in multiple QW c-plane green LEDs.

42 ENGINEERING↗
DOE OSTI2020

A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. Here, the results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Replicating Machine Learning Experiments in Materials Science

Transparency and reproducibility are important aspects of validation for Machine Learning (ML) models that are not fully understood and applies independently of the application domain.We offer a case study of reproducibility that highlights the challenges encountered when attempting to reproduce analyzes obtained with Machine Learning methods in materials informatics. Our study explores prediction results obtained with ML models and issues in training data serving as input. We discuss challenges related to theory-driven and numerical errors in training data, lack of reproducibility across platforms and versions, and effects of randomness when varying hyperparameters. In addition to model accuracy, a main metric of interest in the ML community, our results show that model sensitivity may be equally important for applying ML in domain applications such a materials science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Thermo-Mechanical Phase-Field Modeling of Fracture in High-Burnup UO2 Fuels Under Transient Conditions

This study presents a novel multiphysics phase-field fracture model to analyze high-burnup uranium dioxide (UO2) fuel behavior under transient reactor conditions. Fracture is treated as a stochastic phase transition, which inherently accounts for the random microstructural effects that lead to variations in the value of fracture strength. Moreover, the model takes into consideration the effects of temperature and burnup on thermal conductivity. Therefore, the model is able to predict crack initiation, propagation, and complex morphologies in response to thermal gradients and stress distributions. Several simulations were conducted to investigate the effects of operational and transient conditions on fracture behavior and the resulting cracking patterns. High-burnup fuels exhibit reduced thermal conductivity, elevating temperature gradients and resulting in extensive radial and circumferential cracks. Transient heating rates and temperatures significantly affect fracture patterns, with higher heating rates generating steeper gradients and more irregular crack trajectories. This approach provides critical insights into fuel integrity during accident scenarios and supports the safety evaluation of extended burnup limits.

Chemistry↗