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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Diamagnetic mechanism of critical current non-reciprocity in multilayered superconductors

The suggestion that non-reciprocal critical current (NRC) may be an intrinsic property of non-centrosymmetric superconductors has generated renewed theoretical and experimental interest motivated by an analogy with the non-reciprocal resistivity due to the magnetochiral effect in uniform materials with broken spatial and time-reversal symmetry. Theoretically it has been understood that terms linear in the Cooper pair momentum do not contribute to NRC, although the role of higher-order terms remains unclear. In this work we show that critical current non-reciprocity is a generic property of multilayered superconductor structures in the presence of magnetic field-generated diamagnetic currents. In the regime of an intermediate coupling between the layers, the Josephson vortices are predicted to form at high fields and currents. Experimentally, we report the observation of NRC in nanowires fabricated from InAs/Al heterostructures. The effect is independent of the crystallographic orientation of the wire, ruling out an intrinsic origin of NRC. Non-monotonic NRC evolution with magnetic field is consistent with the generation of diamagnetic currents and formation of the Josephson vortices. This extrinsic NRC mechanism can be used to design novel devices for superconducting circuits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Machine learning of superconducting critical temperature from Eliashberg theory

Abstract The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T c . McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α 2 F ( ω ). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α 2 F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α 2 F functions and numerical solutions of the Eliashberg equations, we derive a formula for T c that performs as well as Allen–Dynes for low- T c superconductors and substantially better for higher- T c ones. This corrects the systematic underestimation of T c while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Charge density waves in cuprate superconductors beyond the critical doping

The unconventional normal-state properties of the cuprates are often discussed in terms of emergent electronic order that onsets below a putative critical doping of x c ≈ 0.19. Charge density wave (CDW) correlations represent one such order; however, experimental evidence for such order generally spans a limited range of doping that falls short of the critical value x c , leading to questions regarding its essential relevance. Here, we use X-ray diffraction to demonstrate that CDW correlations in La 2–x Sr x CuO 4 persist up to a doping of at least x = 0.21. The correlations show strong changes through the superconducting transition, but no obvious discontinuity through x c ≈ 0.19, despite changes in Fermi surface topology and electronic transport at this doping. These results demonstrate the interaction between CDWs and superconductivity even in overdoped cuprates and prompt a reconsideration of the role of CDW correlations in the high-temperature cuprate phase diagram.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lattice-shifted nematic quantum critical point in FeSe 1-x S x

We report the evolution of nematic fluctuations in FeSe 1-x S x single crystals as a function of Sulfur content x across the nematic quantum critical point (QCP) x c ~ 0.17 via Raman scattering. The Raman spectra in the B1g nematic channel consist of two components, but only the low energy one displays clear fingerprints of critical behavior and is attributed to itinerant carriers. Curie–Weiss analysis of the associated nematic susceptibility indicates a substantial effect of nemato-elastic coupling, which shifts the location of the nematic QCP. We argue that this lattice-induced shift likely explains the absence of any enhancement of the superconducting transition temperature at the QCP. The presence of two components in the nematic fluctuations spectrum is attributed to the dual aspect of electronic degrees of freedom in Hund’s metals, with both itinerant carriers and local moments contributing to the nematic susceptibility.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhanced superconductivity and ferroelectric quantum criticality in plastically deformed strontium titanate

The properties of quantum materials are commonly tuned using experimental variables such as pressure, magnetic field and doping. Here we explore a different approach using irreversible, plastic deformation of single crystals. We show that compressive plastic deformation induces low-dimensional superconductivity well above the superconducting transition temperature (Tc) of undeformed SrTiO3, with evidence of possible superconducting correlations at temperatures two orders of magnitude above the bulk Tc. The enhanced superconductivity is correlated with the appearance of self-organized dislocation structures, as revealed by diffuse neutron and X-ray scattering. We also observe deformation-induced signatures of quantum-critical ferroelectric fluctuations and inhomogeneous ferroelectric order using Raman scattering. Our results suggest that strain surrounding the self-organized dislocation structures induces local ferroelectricity and quantum-critical dynamics that strongly influence Tc, consistent with a theory of superconductivity enhanced by soft polar fluctuations. Our results demonstrate the potential of plastic deformation and dislocation engineering for the manipulation of electronic properties of quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A baseline structure inventory with critical attribution for the US and its territories

Leveraging high performance computing, remote sensing, geographic data science, machine learning, and computer vision, Oak Ridge National Laboratory has partnered with Federal Emergency Management Agency (FEMA) to build a baseline structure inventory covering the US and its territories to support disaster preparedness, response, and recovery. The dataset contains more than 125 million structures with critical attribution, and is ready to be used by federal agencies, local government and first responders to accelerate on-the-ground response to disasters, further identify vulnerable areas, and develop strategies to enhance the resilience of critical structures and communities. Data can be freely and openly accessed through Figshare data repository, ESRI’s Living Atlas or FEMA’s Geodata platform.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Climate and Earth system modelling↗
DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Selective bacterial separation of critical metals: towards a sustainable method for recycling lithium ion batteries

The large scale recycling of lithium ion batteries (LIBs) is essential to satisfy global demands for the raw materials required to implement this technology as part of a clean energy strategy. However, despite what is rapidly becoming a critical need, an efficient and sustainable recycling process for LIBs has yet to be developed. Biological reactions occur with great selectivity under mild conditions, offering new avenues for the implementation of more environmentally sustainable processes. Here, we demonstrate a sequential process employing two bacterial species to recover Mn, Co and Ni, from vehicular LIBs through the biosynthesis of metallic nanoparticles, whilst Li remains within the leachate. Moreover the feasibility of Mn recovery from polymetallic solutions was demonstrated at semi-pilot scale in a 30 L bioreactor. Additionally, to provide insight into the biological process occurring, we investigated selectivity between Co and Ni using proteomics to identify the biological response and confirm the potential of a bio-based method to separate these two essential metals. Our approach determines the principles and first steps of a practical bio-separation and recovery system, underlining the relevance of harnessing biological specificity for recycling and up-cycling critical materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An overview of various critical aspects of low-cobalt/cobalt-free Li-ion battery cathodes

Cathodes of lithium-ion batteries (LIBs) significantly impact the environmental footprint, cost, and energy performance of the battery-pack. Hence, sustainable production of Li-ion battery cathodes is critically required for ensuring cost-effectiveness, environmental benignity, consumer friendliness, and social justice. Battery chemistry largely determines individual cell performance as well as the battery pack cost and life cycle greenhouse gas emission. Continuous manufacturing platforms improve production efficiency in terms of product yield, quality and cost. Spent-battery recycling ensures the circular economy of critical elements that are required for cathode production. Innovations in fast-charging LIBs are particularly promising for sustainable e-mobility with a reduced carbon footprint. This article provides an overview of these research directions, emphasizing strategies for low-cobalt cathode development, recycling processes, continuous production and improvement in fast-charging capability.

25 ENERGY STORAGE↗
DOE OSTI2026

Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation

We present a multi-agentic workflow for critical materials recovery that deploys a series of AI agents and automated instruments to recover critical materials from produced water and magnet leachates. This approach achieves selective precipitation from real-world feedstocks using simple chemicals, accelerating the development of efficient, adaptable, and scalable separations to a timeline of days, rather than months and years.

Ritchhart, Andrew J.↗
DOE OSTI2024

A neutron fluence map of the Los Alamos National Laboratory Godiva IV critical assembly

A neutron fluence map and a total ionizing dose map of the Los Alamos National Laboratory Godiva IV fast burst critical assembly was generated using passive reactor dosimetry, comprised of sulfur pellets and thermoluminescent dosimeters. Godiva IV is an unmoderated, fast burst, critical assembly constructed of approximately 65 kg of highly enriched uranium fuel alloyed with 1.5 % molybdenum for strength. The mapping was performed during a single 75.6 ºC temperature rise burst operation, with the top and sides of the cylindrical Godiva-IV Top Hat covered in passive dosimetry. Dosimetry was placed in a symmetric pattern around the Top Hat, with higher concentrations near the control rods and burst rod. A specific portion of the lower quadrant of the burst rod was mapped to confirm a testing region where the neutron fluence varied by no more than ± 5%. The results will be used to assess the neutron, gamma, and total ionizing dose environment in three-dimensional space around the assembly for higher fidelity experiment placement, active dosimetry positioning, and radiation field characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Generation of thermofield double states and critical ground states with a quantum computer

Significance Our experiment prepares two types of nontrivial quantum states on a trapped ion quantum computer: the thermofield double state of the transverse-field Ising model at arbitrary temperature and the quantum critical state of the zero-temperature model. We use techniques motivated by the quantum approximate optimization algorithm, and we implement a hybrid quantum–classical optimization loop to prepare the quantum critical state. Our results pave the way for exploring strongly correlated models at finite temperature and teleportation protocols inspired by black hole physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Nuclear Data–Induced Uncertainties in Criticality Safety Analyses for High-Burnup and Extended Enrichment Fuels

Criticality safety analyses are conducted to show compliance with regulatory standards and to demonstrate safe operational conditions during the storage and transportation of spent nuclear fuel. Given the increased interest in the industry in low-enriched uranium plus (LEU+) and higher-burnup fuel, it is important to study the impact of such fuels’ use on criticality safety analyses and the resulting nuclear data–induced uncertainties. Here, in this work, nominal pressurized water reactor assemblies with LEU+ fuel enrichments up to 8 wt% 235 U and high burnups up to 80 GWd/tonne U were studied. The assemblies were placed in a generic burnup credit cask GBC-32. As a result of the different covariance libraries, using the ENDF/B-VII.1 nuclear data library consistently resulted in lower nuclear data uncertainties than did the use of the ENDF/B-VIII.0 data library. The highest contribution in the nuclear data–induced uncertainties resulted from the major actinides, and their contribution increased with increasing burnup and enrichment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Quasilinear critical gradient model for Alfven eigenmode driven energetic particle transport with intermittency

Abstract A simplified quasilinear critical gradient model (QLCGM) for Alfven eigenmode (AE) driven energetic particle (EP) transport is used to treat the intermittency (burstiness) of the transport. Here intermittency is defined as the ratio of the root mean square deviation from mean flow to the mean flow. The model posits that the intermittency results from the micro-turbulent noise induced in the thermal plasma damping rate for the AE. The model is embedded in a time-dependent EP density transport code to generate the transport-flow time traces from typical critical gradient and slowing down density profiles. The QLCGM appears to be in reasonable agreement with the level and characteristics of the intermittency observed in the DIII-D fast particle loss detector flow time traces: high kurtosis [ O (40)] of the burstiness with the intermittency O (1–2) increasing with the level of mean flow.

Physics↗
DOE OSTI2021

Predicting the critical gradient of ITG turbulence in fusion plasmas

The quasilinear mixing-length approach to efficient prediction of transport in fusion devices is improved to account for the ‘Dimits’ upshift between linear and nonlinear critical pressure gradients in zonal-flow-saturated turbulence regimes. This modification uses the frequency mismatch between modes interacting turbulently to track changes in saturation efficiency. Near criticality, energy is transferred exclusively to stable eigenmodes, rapidly increasing the efficacy of the nonlinearity. Finally, the modified quasilinear model is able to predict below-threshold turbulent ion-temperature-gradient-driven transport accurately and also yields significantly improved predictions for trapped-electron-mode transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The proximity effect and critical field behavior of Re/Al bilayers

Abstract We report the perpendicular critical field H c 2 properties of disordered Re-Al bilayers via magneto-transport measurements. The bilayers consisted of a d Re = 3 nm bottom layer of Re and an upper Al layer with thickness varying between d Al = 0 − 3 nm. We find that in this range of Al thicknesses, the bilayer transition temperature T c increases with increasing Al thickness, although their monolayer counterparts have T c Re > T c Al . Furthermore, H c 2 of the bilayers has a local maximum at an Al coverage of 1.5 nm with a critical field that is 50% larger than that of the standalone 3 nm Re film. At higher Al thicknesses H c 2 drops rapidly but remains more than an order of magnitude greater that that of comparable thickness standalone Al film. Our data show that a thin, disordered Re under-layer can dramatically increase the magnetic field tolerance of the Al over-layer. This would allow one to retain the desirable chemical and metallurgical properties of Al without sacrificing high field compatibility in quantum circuits, such as topological qubit devices and superinductor circuits.

Womack, F. N. (ORCID:0009000810391507)↗
DOE OSTI2025

Charge density fluctuations with enhanced superconductivity at the proposed quantum critical point of Sr0.77⁢Ba0.23⁢Ni2⁢As2

A quantum critical point (QCP) represents a continuous phase transition at absolute zero. In unconventional superconductors, enhanced superconducting transition temperature and magnetic fluctuation strength are often observed together, indicating magnetism-mediated superconductivity. This raises the question of whether quantum fluctuations in other degrees of freedom, such as charge, could similarly boost superconductivity. However, because charge is frequently intertwined with magnetism, isolating and understanding its specific role in Cooper pair formation pose a significant challenge. Here, we report persistent charge density fluctuations (CDFs) down to 15 K in the nonmagnetic superconductor Sr0.77⁢Ba0.23⁢Ni2⁢As2, which lie near a proposed nematic QCP associated with a sixfold enhancement of superconductivity. Our results show that the quasielastic CDFs do not condense into resolution-limited Bragg peaks but, rather, display nonsaturated strength. The phonons associated with CDFs completely soften at 25 K, with their critical behavior described by the same mathematical framework as the antiferromagnetic Fermi liquid model, yielding a fitted Curie-Weiss temperature of 𝜃≈0K. Additionally, we find that the nematic fluctuations are weakly coupled to the lattice, as evidenced by the absence of softening in nematic-coupled in-plane transverse acoustic phonons. Our discovery positions Sr𝑥⁢Ba1−𝑥⁢Ni2⁢As2 as a promising candidate for charge-fluctuation-driven superconductivity.

Aczel, Adam [ORNL] (ORCID:0000000319641943)↗