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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamic Competition between Hubbard and Superexchange Interactions Selectively Localizes Electrons and Holes through Polarons

Controlling the effects of photoexcited polarons in transition metal oxides can enable the long time-scale charge separation necessary for renewable energy applications and controlling new quantum phases through dynamically tunable electron–phonon coupling. In previously studied transition metal oxides, polaron formation is facilitated by a photoexcited ligand-to-metal charge transfer (LMCT). When the polaron is formed, oxygen atoms move away from iron centers, which increases carrier localization at the metal center and decreases charge hopping. Studies of yttrium iron garnet and erbium iron oxide have suggested that strong electron and spin correlations can modulate photoexcited polaron formation. To understand the interplay between strong spin and electronic correlations in highly polar materials, we studied gadolinium iron oxide (GdFeO 3 ), which selectively forms photoexcited polarons through an Fe–O–Fe superexchange interaction. Excitation-wavelength-dependent transient extreme ultraviolet (XUV) spectroscopy selectively excites LMCT and metal-to-metal charge transfer (MMCT) transitions. Here, the LMCT transition suppresses photoexcited polaron formation due to the balance between superexchange and Hubbard interactions, while MMCT transitions result in photoexcited polaron formation within 250 ± 40 fs. Ab initio theory demonstrates that electron and hole polarons localize on iron centers following MMCT. In addition to understanding how strong electronic and spin correlations can control strong electron–phonon coupling, these experiments separately measure electron and hole polaron interactions on neighboring metal centers for the first time, providing insight into a large range of charge-transfer and Mott–Hubbard insulators.

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

Resolving length-scale-dependent transient disorder through an ultrafast phase transition

Abstract Material functionality can be strongly determined by structure extending only over nanoscale distances. The pair distribution function presents an opportunity for structural studies beyond idealized crystal models and to investigate structure over varying length scales. Applying this method with ultrafast time resolution has the potential to similarly disrupt the study of structural dynamics and phase transitions. Here we demonstrate such a measurement of CuIr 2 S 4 optically pumped from its low-temperature Ir-dimerized phase. Dimers are optically suppressed without spatial correlation, generating a structure whose level of disorder strongly depends on the length scale. The redevelopment of structural ordering over tens of picoseconds is directly tracked over both space and time as a transient state is approached. This measurement demonstrates the crucial role of local structure and disorder in non-equilibrium processes as well as the feasibility of accessing this information with state-of-the-art XFEL facilities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Development and Validation of Two-Phase Flow Models in MOOSE for Molten Salt Reactor Application

Two-phase flow in Molten Salt Reactors (MSRs) is important as it impacts reactivity evolution, reactor transient response, and the removal of species dissolved in the molten salt through gas phase transfer. Therefore, accurately predicting the gas distribution and the associated liquid-gas interface area in MSRs is essential for their design and operation. Recently, we integrated two new models into Idaho National Laboratory (INL)’s Multiphysics Object-Oriented Simulation Environment (MOOSE): a multi-D generalization of a mixture drift-flux model and a Euler-Euler model. The Euler-Euler model offers higher fidelity, while the mixture drift-flux model provides greater computational efficiency, which is typically preferred for modeling reactor transients. However, the mixture model's accuracy in capturing void distribution and interfacial area in MSRs still needs to be assessed. This article begins with a description of the mathematical framework for the two-phase models implemented in MOOSE. It then presents validation of these models against relevant experimental data. Finally, both models are applied to the Molten Salt Reactor Experiment case study, analyzing various operational conditions such as different rates of fission product volatilization and diverse cover gas entrainment scenarios at the reactor pump. The article concludes by assessing the suitability of both models for capturing the two-phase flow dynamics critical to MSR operations.

42 - ENGINEERING↗
DOE OSTI2026

Vanishing dynamic strength measured in a transient high-pressure phase of tin

This work reports two independent experimental estimations of the dynamic strength of the body-centered tetragonal (bct) 𝛾 phase of tin, which occurs at pressures above about 9 GPa and transforms promptly back to the ambient 𝛽 phase upon pressure release. Measuring strength in such a transient high-pressure phase is challenging. One measurement used free-surface Richtmyer-Meshkov instabilities generated with gas gun impact. Another set of measurements used ramp-release loading in a pulsed-power facility. Strength estimations came from comparison to simulations using a comprehensive multiphase modeling framework that includes equation of state, shear moduli, and strength separately for each phase, and treats mixed-phase regions. Both experimental methods found the 𝛾-phase strength to be very low, within experimental uncertainty of zero. The existing literature on other materials, by contrast, almost universally reports higher strength in transient high-pressure phases compared with ambient phases. Recent Molecular-Dynamics simulations on tin in the literature showed almost zero deviatoric strength during a deformation-induced bct → bct transformation in which one of the 𝛾-phase 𝑎 axes “flips” to the 𝑐 axis. This reorientation currently provides the most plausible explanation for the low observed strength in 𝛾-phase tin.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development and Validation of Two-Phase Flow Models in MOOSE and Application to Molten Salt Reactors

Two-phase flow in Molten Salt Reactors (MSRs) is important as it impacts reactivity evolution, reactor transient response, and the removal of species dissolved in the molten salt through gas phase transfer. Therefore, accurately predicting the gas distribution and the associated liquid-gas interface area in MSRs is essential for their design and operation. Recently, we integrated two new models into Idaho National Laboratory (INL)’s Multiphysics Object-Oriented Simulation Environment (MOOSE): a multi-D generalization of a mixture drift-flux model and a Euler-Euler model. The Euler-Euler model offers higher fidelity, while the mixture drift-flux model provides greater computational efficiency, which is typically preferred for modeling reactor transients. However, the mixture model's accuracy in capturing void distribution and interfacial area in MSRs still needs to be assessed. This article begins with a description of the mathematical framework for the two-phase models implemented in MOOSE. It then presents validation of these models against relevant experimental data. Finally, both models are applied to the Molten Salt Reactor Experiment case study, analyzing various operational conditions such as different rates of fission product volatilization and diverse cover gas entrainment scenarios at the reactor pump. The article concludes by assessing the suitability of both models for capturing the two-phase flow dynamics critical to MSR operations.

42 - ENGINEERING↗
DOE OSTI2025

Development and Validation of Two-Phase Flow Models in MOOSE and Application to Molten Salt Reactors

Two-phase flow in Molten Salt Reactors (MSRs) is important as it impacts reactivity evolution, reactor transient response, and the removal of species dissolved in the molten salt through gas phase transfer. Therefore, accurately predicting the gas distribution and the associated liquid-gas interface area in MSRs is essential for their design and operation. Recently, we integrated a new two-phase model into Idaho National Laboratory (INL)’s Multiphysics Object-Oriented Simulation Environment (MOOSE): a multi-D generalization of a mixture drift-flux model. It provides greater computational efficiency, which is typically preferred for modeling reactor transients. However, the mixture model’s accuracy in capturing void distribution and interfacial area in MSRs still needs to be assessed. This article begins with a description of the mathematical framework for the two-phase model implemented in MOOSE. It then presents validation of these models against relevant experimental data. Finally, the model is applied to the Molten Salt Reactor Experiment case study, analyzing various operational conditions such as different rates of fission product volatilization and diverse cover gas entrainment scenarios at the reactor pump. The article concludes by assessing the suitability of the mixture drift-flux model for capturing the two-phase flow dynamics critical to MSR operations

42 - ENGINEERING↗
DOE OSTI2026

Machine-learning force-field models for dynamical simulations of metallic magnets

We review recent advances in machine-learning (ML) force-field methods for Landau–Lifshitz–Gilbert simulations of itinerant electron magnets, focusing on their scalability and transferability. Built on the principle of locality, a deep neural-network model is developed to efficiently and accurately predict electron-mediated forces governing spin dynamics. Symmetry-aware descriptors constructed through a group-theoretical approach ensure rigorous incorporation of both lattice and spin-rotation symmetries. The framework is demonstrated using the prototypical s-d exchange model widely employed in spintronics. ML-enabled large-scale simulations reveal novel nonequilibrium phenomena, including anomalous coarsening of tetrahedral spin order on the triangular lattice and the freezing of phase-separation dynamics in lightly hole-doped, strong-coupling square-lattice systems. These results establish ML force-field frameworks as scalable, accurate, and versatile tools for modeling nonequilibrium spin dynamics in itinerant magnets.

Artificial neural networks↗
DOE OSTI2025

Spatiotemporal quenches for efficient critical ground state preparation in the two-dimensional transverse field Ising model

Quantum simulators have the potential to shed light on the study of quantum many-body systems and materials, offering unique insights into various quantum phenomena. Although adiabatic evolution has been conventionally employed for state preparation, it faces challenges when the system evolves too quickly or the coherence time is limited. In such cases, shortcuts to adiabaticity, such as spatiotemporal quenches, provide a promising alternative. This paper numerically investigates the application of spatiotemporal quenches in the two-dimensional transverse field Ising model with ferromagnetic interactions, focusing on the emergence of the ground state and its correlation properties at criticality when the gap vanishes. We demonstrate the effectiveness of these quenches in rapidly preparing ground states in critical systems. Our simulations reveal the existence of an optimal quench front velocity at the emergent speed of light, leading to minimal excitation energy density and correlation lengths of the order of finite system sizes we can simulate. These findings emphasize the potential of spatiotemporal quenches for efficient ground state preparation in quantum systems, with implications for the exploration of strongly correlated phases and programmable quantum computing.

2-dimensional systems↗
DOE OSTI2025

Transient Terahertz Oscillations During Photoinduced Polarization Topology Reconfiguration in Ferroelectric Superlattices

Terahertz resonances embedded in crystalline heterostructures could close a spectral gap between conventional electronics and photonics while opening new windows on non-equilibrium lattice dynamics. We show that femtosecond optical screening of the depolarization field in epitaxial PbTiO3/SrTiO3 superlattices launches a collective polar mode that oscillates near 1 THz and coherently spans the entire mini-Brillouin zone. Wave-vector-resolved pump–probe X-ray diffraction resolves a nearly dispersion-less oscillation at 0.87 THz and 0.94 THz at the zone boundary and zone center, respectively, persisting for ~2.5 ps, corresponding to a weakly damped resonance. Dynamical phase-field simulations reveal the origin of the mode to mesoscopic rotation of closure-domain textures during the photo-excited transition from an unscreened to a screened electrostatic state. Varying the PbTiO3 and SrTiO3 ratio tunes the mode frequency continuously from 0.9 to 1.4 THz, providing a quantitative design rule for frequency-selectable THz oscillators in ferroelectric heterostructures. By coupling nanoscale polarization reconfiguration to long-wavelength coherent dynamics, this work establishes depolarization-field engineering to topology-driven THz functionality and expanding the landscape of collective lattice dynamics.

Sri Gyan, Deepankar [Univ. of Wisconsin, Madison, ↗
DOE OSTI2026

Decoding THz‐Driven Dynamic Fingerprints of Ferroelectric Nanotwin Networks

Ultrafast polarization dynamics in ferroelectrics are of considerable interest for high-speed tunable dielectrics and electro-optics. Extended domain wall networks formed in ferroelectric twin nanodomains can support collective dynamics in the terahertz regime but require techniques that track polarization and strain evolution driven by ultrafast stimulus. Here, we use multi-modal probing of THz-pulse-driven excitations in PbTiO 3 /SrTiO 3 superlattices by combining X-ray free electron laser measurements that directly tracks lattice changes, with optical second harmonic generation that tracks the electronic potential coupled with the lattice potential. Dynamical phase-field modeling enables fingerprinting of these collective modes as superpositions of domain “breathing” through wall oscillations and polarization “rotations” with still walls. Ultrafast domain wall motion at 0.1–0.5 THz is observed at practical fields of 100 kV/cm with wall velocities of >4000 m/s, approaching typical speed of sound in PbTiO 3 . A unique “charging” mode is discovered that can electrically charge and discharge domain walls on ∼4 ps time scale thus dynamically tuning wall conductivity. Integrated experimental and theoretical fingerprinting of the dynamical landscape presented here enables ultrafast control of ferroics for high-speed microelectronics and optical applications.

THz dynamics↗
DOE OSTI2025

Momentum space AC Josephson effect and intervalley coherence in multilayer graphene

Electron transport driven by the phase coherence and interference of quantum many-body wavefunctions is a fascinating phenomenon with potential technological significance. Superconductivity, for example, enables dissipationless transport through macroscopic phase twisting, while charge-density waves show broadband noise and AC-DC interference once their phase is depinned. We identify an analogous phase-coherent dynamics in the intervalley-coherent (IVC) state frequently observed in rhombohedral multilayer graphene. We show that a static magnetic field drives an oscillating intervalley current, which produces an oscillating orbital magnetization and thereby a detectable AC Hall response. This mechanism is similar to the Josephson effect observed in superconductors but now happening in momentum space. In this analogy, the static magnetic field acts as the DC voltage, while the oscillating intervalley current is the AC Josephson current. We provide microscopic calculations of all the phase-number free-energy parameters in rhombohedral trilayer graphene, establishing this effect as an experimental signature of IVC order.

electronic properties and materials↗
DOE OSTI2024

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here, in this work, we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

four-dimensional scanning transmission electron mi↗
DOE OSTI2024

Unveiling the Electrocatalytic Hydrogen Evolution Reaction Pathway on RuP 2 through Ab Initio Grand Canonical Monte Carlo

In this study, the high catalytic reactivity of ruthenium phosphide (RuP 2 ) has been identified by first-principles density functional theory (DFT) calculations for the electrocatalytic hydrogen evolution reaction (HER). Complex surface reconstructions are considered by applying the ab initio grand canonical Monte Carlo (ai-GCMC) algorithm, efficiently providing a sufficient phase-space exploration of possible surfaces. Combined with surface-phase Pourbaix diagrams, we are able to identify the actual surfaces that obtained under specific experimental environments, thus leading to a more accurate understanding of the nature of the active sites and the binding strength of adsorbates. Specifically, through hundreds of surface reconstructions and hydrogenation states generated with ai-GCMC, we identify the most favorable surface phases of RuP 2 under aqueous acidic conditions. We discover that the HER activity is determined by multiple surfaces with different stoichiometries within a narrow electrode potential window. Low HER overpotential (η) has been found for each of the identified surfaces, as low as 0.04 V. High H-coverage reconstructed surfaces have been discovered under acidic conditions, and the surface Ru sites introduced by additional Ru adatoms or exposed by P-vacancies serve as the active sites for HER based on their nearly reversible H binding. Furthermore, this work provides atomistic insights into the origin of high HER activity on RuP 2 by exploring the dynamic surface phases of electrocatalysts and features a generalizable method to explore the reconstructed/hydrogenated surface space as a function of experimental conditions.

25 ENERGY STORAGE↗
DOE OSTI2026

Machine learning-based interatomic potential development and phase transition analysis of ferroelectric hafnium dioxide

The ferroelectric phase (𝑃⁢𝑐⁢𝑎⁢2 1 , which is in orthorhombic symmetry) of hafnium dioxide (HfO 2 ) has gained much attention due to its potential applications in nanoelectronics and advanced memory devices. However, its complex phase behavior under external stimuli, such as pressure and temperature, remains a subject of intense investigation. This study focuses on developing a machine learning-based interatomic potential (MLIP) that is trained with data from density-functional theory (DFT) calculations to simulate phase transitions and mechanical properties of HfO 2 . The developed MLIP predicts lattice parameters, equations of state, bulk and shear moduli, and elastic constants that closely align with DFT predictions for several phases and at various pressures. Once validated, the MLIP is used to investigate the phase transitions of ferroelectric HfO 2 (𝑃⁢𝑐⁢𝑎⁢2 1 ) under both isobaric and constant stress conditions at elevated temperatures ranging from 200 to 2500 K. We used several complementary methods, including local symmetry identification, radial distribution function, and x-ray diffraction characterization, to identify interesting phase transitions among several competitive hafnia phases predicted from our simulations. The suggested methods uniformly reveal that under pure deviatoric condition, the system favors a transition from the orthorhombic 𝑃⁢𝑐⁢𝑎⁢2 1 phase to a tetragonal (𝑃⁢4 2 /𝑛⁢𝑚⁢𝑐) phase, whereas a zero stress condition drives the system from the 𝑃⁢𝑐⁢𝑎⁢2 1 phase to another orthorhombic (𝑃⁢𝑏⁢𝑐⁢𝑛) phase. These findings provide crucial insights into stress and temperature-induced phase behavior of hafnia, guiding future experimental and theoretical studies for optimizing hafnia-based ferroelectric devices.

Ferroelectric HfO2↗
DOE OSTI2024

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Atomic-scale insights into topotactic transformations in an extra-large-pore zeolite using time-resolved 3D electron diffraction

Understanding the atomic-scale structural dynamics of phase transformations is crucial for developing materials and tailoring their properties. However, many materials are obtained as polycrystalline powders with large unit cells and/or complex structures, making it challenging to investigate detailed structural changes using conventional X-ray diffraction techniques. Here we employ time-resolved three-dimensional electron diffraction to reveal the topotactic reactions and transformations that convert the extra-large-pore silicate zeolite ECNU-45 into ECNU-46. ECNU-45 features three-dimensional interconnecting 24 × 10 × 10-ring channels, while ECNU-46 consists of one-dimensional 24-ring channels connected to 10-ring pockets. ECNU-45 and ECNU-46 are both examples of pure silicate zeolites with pore openings larger than 22-ring. Our findings indicate changes at six distinct tetrahedral silicon sites, involving atom displacement, addition and removal of framework atoms through bond breakage and formation. This work presents the synthesis of zeolites and also provides atomic-level insights into the dynamic processes of topotactic reactions. Our results have implications for advancing materials engineering and understanding complex solid-state reactions at an atomic scale.

Inorganic chemistry↗
DOE OSTI2024

Evaluate Synergies of Using Hydrothermal Liquefaction and Anerobic Digestion Treatment Technologies for Wastewater Resource Recovery Facilities (CRADA 516 Final Report)

The research focuses on utilizing a new anaerobic digestion (AD) configuration to treat the aqueous by-product generated by hydrothermal liquefaction (HTL) of sewage sludge. This report found that for Anaerobic Digestion for HTL By-product, Anaerobic biofilms can degrade some HTL wastewater contaminants, but co-digestion is essential to address nutrient deficiencies and optimize performance. Without AD, toxicity of HTL aqueous streams may limit broader adoption in wastewater treatment plants (WWTPs). Great Lakes Water Authority (GLWA) used an innovative reactor design, involving a dynamic membrane anaerobic bioreactor to promote biofilm growth, improving contaminant degradation. The tree-like structure inside the reactor supports biofilm development with recirculation enhancing microbial activity. Overall, a 70% chemical oxygen demand (COD) removal was achieved, although nutrient supplementation is required for stability. The reactor achieved a diverse microbial community, including methanogens and bacteria capable of degrading phenols and aromatics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Observing Quantum Measurement Collapse as a Learnability Phase Transition

During a quantum measurement, superpositions of states with different observable properties probabilistically collapse into one with a sharp value of the measured observable. In macroscopic quantum systems, this collapse arises via a continuous measurement-induced phase transition (MIPT) at a critical value of the strength of interaction with the measurement apparatus. MIPTs lie outside established paradigms for equilibrium or nonequilibrium critical phenomena and delineate distinct, stable dynamical and computational phases of matter. Quantum computers enable programmable simulation of the interaction of a measurement apparatus with a dynamical quantum system, to explore MIPT phenomena over a range of system sizes while retaining quantum coherence. Yet, existing experimental protocols rely on fundamentally nonscalable postselection techniques or direct classical simulation of quantum circuits. Here, we report the scalable observation of finite-size scaling evidence for an observable-sharpening MIPT in monitored quantum circuits in a chain of Yb + 171 ions in Quantinuum’s H1-1 trapped-ion quantum processor. By leveraging an equivalent description as a statistical physics problem, we implement scalable classical algorithms to infer the value of the measured observable from a single experimental shot. This technique enables a truly scalable protocol to observe observable-sharpening MIPTs in generic classes of circuits that cannot be directly classically simulated and also provides enhanced means to detect and suppress errors in the quantum simulation. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗