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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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804 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimizing Porous Transport Layer Porosity for Proton Exchange Membrane Water Electrolysis

An empirical model is presented that describes anode-side losses related to porous transport layer (PTL) morphology in proton exchange membrane water electrolysis (PEMWE). The model is based on an advanced voltage breakdown analysis that links various overpotentials to PTL morphology. Custom Ti PTLs, spanning uncommonly low porosities (22 - 31%), were fabricated and analyzed with X-ray CT to obtain pore and particle size distributions. Particle size distributions were consistent across samples with an average particle diameter of 12.0?..mu..m, whereas average pore diameters ranged from 6.0 to 7.0?..mu..m. The PTLs were tested in standard PEMWE cell assemblies with anode catalyst loadings of 0.1 mgIr cm-2 to obtain polarization curves, electrochemical impedance spectra, and augmented Tafel analysis. The PTL-dependent anode side losses were deconvoluted and assigned to excess utilization, concentration, ion transport resistance, and electrical contact resistance overpotentials. The data and model reveal an optimal 20 - 28% PTL porosity region where utilization and contact resistance overpotentials are minimized without triggering concentration and ion transport losses related to water deprivation. The optimal PTL porosity depends on the operating current density and is demonstrated at realistic PEMWE water flow rates to establish PTL design guidance for operation at scale.

08 HYDROGEN↗
DOE OSTI2026

Integral Nuclear Data and Benchmarking Needs for Fusion Energy Systems

Fusion energy systems are currently being designed and optimized using radiation transport codes. To deal with the unique environment inside a fusion-based system, many of these designs incorporate novel materials able to withstand the high radiation fields, ensure adequate cooling and thermal protection, and produce tritium. Validation plays a vital role in building trust in the predictive power of these models and computational methods. Validation of a code consists of modeling documented real-world experiments and comparing the code-predicted response to the measured response. Adequate validation requires measured responses from real-world experiments, also known as integral data, that mimic the system being designed, including materials, impinging radiation, and temperature, among other variables. The most trusted integral data are experimental responses that have been through a rigorous benchmarking process that develops a recommended computational model and evaluates all experimental uncertainties. Finally, there are a few research groups around the world that have been producing integral data for fusion applications, but a substantial investment is needed to address the unique validation needs of the fusion community.

Fusion↗
DOE OSTI2026

Process control-enabled mitigation of microstructural and plastic heterogeneities in additively manufactured Grade 91 steel

Synergizing wire arc-directed energy deposition (WA-DED) additive manufacturing (AM) with particle-strengthened creep strength-enhanced ferritic (CSEF) steels enables fabrication and repair of critical power-plant components. Investigations focused on fusion-welded particle-strengthened CSEF steels, such as Grade 91 steel, have linked microstructurally heterogeneous regions—forming due to heat affected zones (HAZ)—with premature failure during elevated temperature service. Fusion-based AM, including WA-DED, likewise generates microstructurally and plastically heterogeneous regions due to spatiotemporally varying thermokinetics during deposition. However, works investigating such microstructural heterogeneities, their implications for mechanical behavior, and strategies to mitigate their formation remain scarce. This work identifies microstructurally and plastically heterogeneous regions within the WA-DED-processed Grade 91 steel. Spatial microhardness variations in the as-fabricated specimen correlate with the variation in the attributes of grain, martensitic microstructure, and precipitates across the fusion zone and HAZ. Digital image correlation-enabled tensile tests performed at 500 °C revealed pronounced deformation localization and a wave-like strain distribution, with wavelength close to the melt pool depth, indicating susceptibility of the as-fabricated components to premature creep failure. Such heterogeneity in microstructural and mechanical behavior was attributed to recurring solid-state phase transformations. Subsequently, an interlayer temperature control strategy was implemented, wherein maintaining interlayer temperature above the martensitic start temperature mitigated the heterogeneous microstructural and plastic response in the as-fabricated condition. Findings open pathways to achieving deformation-localization- and creep-resistant microstructures in WA-DED fabricated particle-strengthened CSEF steel components, reducing reliance on post-welding heat treatments—conventionally required to enhance creep resistance—and enabling on-demand, short lead-time fabrication of next-generation power-plant components.

Heat affected zones↗
DOE OSTI2026

String-Breaking Dynamics in Quantum Adiabatic and Diabatic Processes

Confinement prohibits isolation of color charges, e.g., quarks, in nature via a process called string breaking : the separation of two charges results in an increase in the energy of a color flux, visualized as a string, connecting those charges. Eventually, creating additional charges is energetically favored, hence breaking the string. Such a phenomenon can be probed in simpler models, including quantum spin chains, enabling enhanced understanding of string-breaking dynamics. A challenging task is to understand how string breaking occurs as time elapses, in an out-of-equilibrium setting. This work establishes the phenomenology of dynamical string breaking induced by a gradual increase of string tension over time. It, thus, goes beyond instantaneous quench processes and enables tracking the real-time evolution of strings in a more controlled setting. We focus on domain-wall confinement in a family of quantum Ising chains. Our results indicate that, for sufficiently short strings and slow evolution, string breaking can be described by the transition dynamics of a two-state quantum system akin to a Landau-Zener process. For longer strings, a more intricate spatiotemporal pattern emerges: the string breaks by forming a superposition of bubbles (domains of flipped spins of varying sizes), which involve highly excited states. We finally demonstrate that string breaking driven only by quantum fluctuations can be realized in the presence of sufficiently long-ranged interactions. This work holds immediate relevance for studying string breaking in quantum-simulation experiments.

Ising model↗
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Electrolyte-Induced Restructuring of Acid-Stable Oxygen Evolution Catalysts

Crystalline metal oxide catalysts operating under oxygen evolution reaction (OER) conditions invariably restructure, resulting in active sites with hydroxo/oxo species in an amorphous environment. An increase in the population of terminal hydroxo/oxo species (i.e., edge sites) facilitates proton-coupled electron-transfer (PCET) kinetics for oxygen generation and thus improves catalyst competency. While amorphous films benefit from a greater density of active sites, they suffer from diminished charge transport as compared to that of extended crystalline lattices. Managing this amorphous–crystalline dichotomy is essential when designing OER catalysts, which we highlight with the examination of electrodeposited PbO x materials, which historically are very poor OER catalysts. Along these lines, the presence of phosphate during PbO x electrodeposition truncates the growth of an extended lattice owing to its strong bonding to oxide surfaces to afford an amorphous catalyst film (A-PbO x ) with significant charge-transfer resistance (138 ± 42 Ω) and poor OER kinetics (420 ± 105 mV dec –1 Tafel slope). Conversely, electrodeposition of Pb 2+ in the presence of less coordinating electrolytes such as nitrate affords crystalline β-PbO2 with improved charge-transfer resistance (42.6 ± 1.1 Ω), though still poor OER kinetics (134 ± 36 mV dec –1 Tafel slope). By operating amorphous A-PbOx in less coordinating electrolytes, however, a new partially crystalline material can be generated (μc-PbO x ) with further reduced charge-transfer resistance (33.0 ± 1.4 Ω) and improved OER kinetics (70 ± 15 mV dec –1 Tafel slope). The enhanced OER activity of μc-PbO x is the result of coupling the high edge-site population of an amorphous PbOx phase with crystalline-like charge transport properties. Finally, the ability to use an electrolyte to induce OER activity in an inactive amorphous form of PbO x highlights the benefits of optimizing the amorphous–crystalline phase compositions in the design of active OER catalysts.

catalysts↗
DOE OSTI2026

Galaxy Size and Rotation Curve Diversity in ΛCDM with Baryons

The observed rotation curves of dwarf galaxies exhibit significant diversity at fixed halo mass, challenging galaxy formation within the cold dark matter (CDM) model. Previous cosmological galaxy formation simulations with baryonic physics fail to reproduce the full diversity of rotation curves, suggesting that there is a flaw in baryonic feedback models, observational bias, or that an alternative to CDM must be invoked. In this work, we use the Marvelous Massive Dwarf zoom-in simulations, a suite of high-resolution dwarf simulations with M 200 ∼ 10 10 –10 11 M ⊙ and M * ∼ 10 7 –10 9 M ⊙ , designed to target the mass range where the galaxy rotation curve diversity is maximized, i.e., between and 100 km s −1 . We add to this a set of low-mass galaxies from the Marvel Dwarf Zoom Volumes to extend the galaxy mass range to lower values. Our fiducial star formation and feedback models produce simulated dwarfs with a broader range of rotation curve shapes, similar to observations. These simulations both create dark matter cores via baryonic feedback, reproducing the slower-rising rotation curves, while also allowing for compact galaxies and steeply rising rotation curves. Our simulated dwarfs also reproduce the observed size–M * relation, including scatter, producing both extended and compact dwarfs for the first time in simulated field dwarfs. However, the slowly rising and high baryon mass fraction, as well as the steeply rising and low baryon mass fraction, remain missing. We explore star formation and feedback models and conclude that previous simulations may have had feedback that was too strong to produce compact dwarfs.

Cruz, Akaxia [Flatiron Institute, New York, NY (Un↗
DOE OSTI2026

Massive neutrinos and interacting dark matter look alike through the lens of lensing

Here, we demonstrate that the suppression in the lensing power spectrum of the cosmic microwave background (CMB) caused by massive neutrinos can be mimicked by dark matter-baryon interactions at the precision of next-generation CMB experiments. Thus, a determination of neutrino masses from the CMB lensing power spectrum may be compromised. We illustrate the degeneracy for a dark matter-proton cross section ∝ $v$ –4 , which arises in the t-channel exchange of an ultralight mediator in the nonrelativistic limit.

cosmological parameters↗
DOE OSTI2025

Challenges in Continuous In-Field Critical Current Testing of High-Temperature Superconducting Tapes: Thermal and Mechanical Perspectives

High-temperature superconductors (HTS) are essential for ultra-high-field applications requiring exceptional current-carrying capacity under extreme conditions. However, systematic characterization of critical current in long-length conductors remains challenging due to complex thermal, electromag netic, and mechanical interactions during continuous testing. This study reports the development of a continuous in-field magnetization testing system for position-dependent critical current measurement in HTS tapes at 20 K under 7.5 T fields applied normal to the tape plane, enabling identification of performance-limiting regions that could compromise magnet stability. Here, the system addresses two fundamental challenges inherent to cryogenic reel to-reel testing. First, thermal management requires continuous cooling of a moving conductor to 20 K, achieved through liquid nitrogen precooling combined with a 100 W@20 K Gifford McMahon cryocooler. Second, screening currents in high fields generate Lorentz forces that induce twisting, bowing, and potential delamination. To mitigate these risks, we propose mechanical reinforcement and active current density suppression strategies. Numerical simulations using the stream function formulation reveal four primary failure modes: frictional heating at guide interfaces, unstable equilibria causing deformation, transverse current-induced stresses at guide transitions, and unsupported forces in vertical spans. Our mitigation strategies include PTFE coated guides to minimize friction, spring-loaded stabilization mechanisms to maintain tape alignment, controlled pre-heating using the liquid nitrogen thermal jacket to suppress critical current at stress points, and optimized guide positioning to minimize force accumulation. The experimental system is nearing completion, with testing planned to commence within two months. Preliminary validation at 65 K under 0.5 T demonstrates strong correlation between simulation-predicted mechanical instabilities and observed critical current variations during conductor tran sitions through the measurement region. These findings establish a robust foundation for quality assurance protocols essential to next-generation superconducting magnet applications.

Chen, Siwei [Princeton Plasma Physics Laboratory (↗
DOE OSTI2025

Methodology of Atomic Force Microscopy Visualization of Electrode–Electrolyte Interfaces

Electrochemical atomic force microscopy (EC-AFM) provides unprecedented insights into the microstructure of electrode–electrolyte interfaces during electrochemical reactions. However, performing EC-AFM measurements has many challenges, for example, drift, contamination, and probe degradation. We present solutions to these experimental issues through electrochemical cell design and carefully chosen experimental parameters. The possibility that the probes can react with the interface during scanning, generating false-positive electrochemical dynamics, is discussed as an example of the challenge of high-fidelity EC-AFM measurement. Here, we demonstrate this effect in highly ordered pyrolytic graphite and show that we could use electrochemical control of the AFM probe to enable high-fidelity in situ AFM visualization of solid–liquid interfaces during electrochemical reactions.

Electrochemical cells↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2026

Digital Twin Based Condition Monitoring of LCC-LCC Inductive Power Transfer Systems

Inductive power transfer (IPT) systems provide a flexible, hands-free charging opportunity to electric vehicles (EV). The resonant network components and the transmitter and receiver coils are often subjected to high voltages or currents. Component aging in the compensation network and coils of resonant IPT systems is detrimental to the reliability and power transfer efficiency of the IPT system. Monitoring the component health of such multi-element complex systems requires robust optimization algorithms. This paper discusses condition monitoring of a resonant IPT system for an EV charger using a digital twin model. A hybrid estimation algorithm based on genetic algorithms and adaptive particle swarm optimization is developed to estimate the parameters of the digital twin model. Simulation results are used to verify the monitoring capabilities of the developed algorithm under various operating conditions of the IPT system.

Weldehawaryat, Lidya Mussie [graduate research ass↗
DOE OSTI2026

Multi-purpose quantum laboratories from superconducting circuits

Superconducting circuits (SCs) are the cornerstone of modern quantum technology, enabling scalable computing through coherent control of macroscopic quantum states. Through a legacy that predates modern quantum computing, SCs have emerged as high-precision instruments for discovery. In this review, we highlight the role of SCs as general-purpose quantum laboratories, outlining the emerging landscape of correlated matter-circuit science. We review and unify the capabilities of superconducting quantum hardware across condensed matter, high energy and quantum information sciences. We trace the technical evolution of these architectures, illustrating how their foundational development has culminated in a toolkit for resolving the complexities of macroscopic quantum states.

Arora, Arpit [UCLA, Los Angeles (main); UCLA; Haim↗
DOE OSTI2024

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions

Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 μ B . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state.

Computer Science↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Photoelectrochemical Proton-Coupled Electron Transfer of TiO 2 Thin Films on Silicon

TiO 2 thin films are often used as protective layers on semiconductors for applications in photovoltaics, molecule–semiconductor hybrid photoelectrodes, and more. Experiments reported here show that TiO 2 thin films on silicon are electrochemically and photoelectrochemically reduced in buffered acetonitrile at potentials relevant to photoelectrocatalysis of CO 2 reduction, N 2 reduction, and H 2 evolution. On both n-type Si and irradiated p-type Si, TiO 2 reduction is proton-coupled with a 1e – :1H + stoichiometry, as demonstrated by the Nernstian dependence of the Ti 4+/3+ E 1/2 on the buffer pK a . Experiments were conducted with and without illumination, and a photovoltage of ∼0.6 V was observed across 20 orders of magnitude in proton activity. The 4 nm films are almost stoichiometrically reduced under mild conditions. The reduced films catalytically transfer protons and electrons to hydrogen atom acceptors, based on cyclic voltammogram, bulk electrolysis, and other mechanistic evidence. TiO 2 /Si thus has the potential to photoelectrochemically generate high-energy H atom carriers. Characterization of the TiO 2 films after reduction reveals restructuring with the formation of islands, rendering TiO 2 films as a potentially poor choice as protecting films or catalyst supports under reducing and protic conditions. Altogether, this work demonstrates that atomic layer deposition TiO 2 films on silicon photoelectrodes undergo both chemical and morphological changes upon application of potentials only modestly negative of RHE in these media. While the results should serve as a cautionary tale for researchers aiming to immobilize molecular monolayers on “protective” metal oxides, the robust proton-coupled electron transfer reactivity of the films introduces opportunities for the photoelectrochemical generation of reactive charge-carrying mediators.

Electrodes↗
DOE OSTI2026

Small-molecule modulation of β-arrestins

β-Arrestins are multifunctional regulators of G-protein-coupled receptor (GPCR) signalling and orchestrate diverse downstream signalling events and physiological responses across the GPCR superfamily. Although GPCR pharmacology has advanced to target orthosteric and allosteric sites, as well as G proteins and GPCR kinases, direct chemical tools to modulate β-arrestin activities have remained conspicuously absent. Here we report the identification of small-molecule inhibitors that selectively target β-arrestins and delineate their mechanism of action through integrated pharmacological, biochemical, biophysical and structural analyses. These inhibitors disrupt β-arrestin engagement with agonist-activated GPCRs, impairing desensitization, internalization and β-arrestin-dependent physiological functions while sparing G protein–receptor coupling. Cryo-electron microscopy, molecular dynamics simulations and structure-guided mutagenesis reveal that one modulator, Cmpd-5, engages a pocket within the central crest of β-arrestin1 formed by the middle, C and lariat loops, a critical receptor-binding interface, stabilizing a distinct conformation that is incompatible with full β-arrestin–receptor engagement. Together, these findings establish a mechanistic framework for β-arrestin modulation, reveal a novel allosteric site for structure-based drug design, and open new avenues for transducer-targeted, pathway-specific GPCR therapeutic agents.

Kahsai, Alem W. [Duke University, Durham, NC (Unit↗
DOE OSTI2026

Review of Shock, Vibration, and Other Tests for Type IV Hydrogen Composite Pressure Vessels for Rail Use

Requirements specific to hydrogen rail transportation and its unique components are currently not well represented in safety codes and standards. Notably, one of the most important components of the system, the hydrogen storage vessel, may be subject to entirely different shock and vibration conditions compared to hydrogen tanks on road vehicles. This report focuses on how composite hydrogen tanks are tested for performance and how this may relate to these tanks being used on rail systems. The shock and vibration testing procedures that are relevant for hydrogen composite tanks used for rail applications have been identified, as well as some general testing requirements for hydrogen tanks and composite tanks.

42 ENGINEERING↗