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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamic compression effects of H 2 ⁡O in a dynamic diamond anvil cell: Origin of metastable ice VII and its crystal growth kinetics

We report on the structural verification of metastable ice VII solidifying in the phase space of ice VI at 1.80 GPa at room temperature. Using time-resolved (TR) x-ray diffraction and TR ruby luminescence paired with high-speed microphotography utilizing a dynamic diamond anvil cell, an initial compression rate range from 0.12 to 95.84 GPa/s was explored. The solidification pressure of metastable ice VII has a potential sigmoidal dependence upon compression rate with a turnover compression rate of ∼80 GPa/s. The preferred crystallization of ice VII in the stability field of ice VI is due to the increased nucleation rate of ice VII over ice VI at 1.77 GPa that is driven by the surface energy difference between the liquid and solid phases along with the change in Gibbs free energy of solidification. The dynamic pressure-volume–compression behaviors of ice phases (VI and VII) show a lattice stiffening in both phases, especially during the compression loading. It is also found that the compression rate greatly affects the solid-solid phase transition between ice VI and VII but does not affect the liquid-solid transition between water and ice VI as much. Lastly, a third phase transition was found to occur after metastable ice VII transforms into high-density amorphous (HDA) ice, which could be a disordered hydrogen-bonded network configuration of ice VII forming out of HDA ice facilitated by the decoupling of the oxygen movement and reorientation of the H 2⁡ O molecule. These results demonstrate the complexity of a seemingly simple molecule H 2⁡ O, how it can readily change its static properties with the modification of (de)compression rate, and highlight the need to use multiple TR structural and spectroscopic probes at higher time resolutions to realize the most comprehensive understanding.

Chemical bonding↗
DOE OSTI2026

Temporal and spatial separations between spin glass and short-range order

Broken-symmetry-induced order parameters account for many phenomena in physics. For spin glasses, this framework dictates the theoretical construction, whereas experiments have only established dynamical behaviors but not the physical entity. Experimental techniques have limitations when the spin glass is probed as an isolated state. Here, we create an evolution from a long-range order using well-controlled non-magnetic substitution on a sublattice. Neutron magnetic diffuse scattering of pico-second timescale reveals that the dynamics of short- and long-range order formation are not affected by disorder, but their spatial ranges are. Across all specimens, the inflection point of spin correlation length’s temperature dependence fully matches with the peak in heat capacity, while spin glass can freeze at millisecond timescale either above or below this characteristic temperature of spin-order formation. Our results identify the spin glass as single, uncorrelated spins at domain walls between spin clusters, and an uncorrelated coexistence of the two.

Dronova, Margarita G [Okinawa Institute of Science↗
DOE OSTI2025

Control of solvent adsorption in porous liquids through solvent-solvent interactions

Type 3 Porous Liquids (PLs) are a new class of materials that offer transformative potential for gas capture and utilization. These PLs are comprised of a bulky solvent with a suspended empty nanoporous material that enables selective and high-capacity capture from dilute and complex gas steams. The relationship between the nanoporous material, the structure of the solvent molecules, and the time dependence of solvent adsorption into the nanoporous material governs long term adsorption properties. Herein, molecular dynamics (MD) calculations evaluated the solvent dynamics of nine neat solvents with a broad range of chemical identities and experimental density measurements probed the time-dependent adsorption of these solvents into the ZIF-8 metal-organic frameworks (MOFs) to form PL dispersions over three weeks. Two of the nine dispersion, using glyceryl triacetate and 2’-hydroxyacetopheneone as solvents, presented sufficiently low solvent infiltration—characterized by solvent adsorption less than 40% of the ZIF-8 pore volume—to be viable as PLs. The experimental solvent adsorption data for ZIF-8 dispersed in the four aromatic solvents (acetophenone, methylbenzoate, 2-isopropylphenol, and 2’-hydroxyacetophenone) was fit to a kinetic model to quantify rate of solvent adsorption into ZIF-8. The rates of adsorption of these four similar-sized solvents demonstrated that solvent adsorption is slowed and limited by solvent clustering dynamics that are, in turn, driven by intermolecular hydrogen bonding. Ultimately, experimental solvent sorption measurements indicated that glyceryl triacetate and 2’-hydroxyacetophenone with ZIF-8 formed PLs with the most stable porosity and the greatest potential for gas capture. In conclusion, the combined experimental-computational materials exploration framework used here reveals a novel relationship between molecular scale solvent dynamics and macroscale solvent adsorption kinetics critical for the discovery and rapid evaluation of Type 3 PL compositions.

Hydrogen bonding↗
DOE OSTI2025

Insights from Femtosecond Transient Absorption Spectroscopy into the Structure–Function Relationship of Glyceline Deep Eutectic Solvents

This study aimed to determine the structure–function relationship (SFR) for ChCl–glycerol mixtures, a deep eutectic solvent (DES), by investigating their microscopic solvation dynamics and how it relates to their macroscopic properties across varying concentrations of ChCl. Femtosecond transient absorption (fs-TA) spectroscopy revealed two distinct solvation dynamics time constants: τ 1 , governed by glycerol–glycerol interactions, and τ 2 , dominated by the choline response. The τ 2 minimum at 25–30 mol % ChCl closely aligned with the eutectic composition (~33.33 mol % ChCl), where the glycerol network was the most organized and the choline ions exhibited the fastest relaxation. The viscosity decreased sharply up to ~25 mol % ChCl and then plateaued, while the conductivity increased monotonically with ChCl concentration, reflecting enhanced ionic mobility. The density decreased with both increasing ChCl concentration and temperature, indicating disrupted hydrogen bonding and reduced molecular packing. The polarity, measured using betaine-30 (B30) and the E T (30) polarity scale, increased steeply up to approximately 25 mol % ChCl before reaching a plateau. These findings identified the eutectic composition as the optimal concentration range for balancing stability, fluidity, conductivity, and enhanced dynamics within the glycerol system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

How Proton Incorporation Reshapes Lattice Dynamics In BaSnO 3 ‐Type Proton Conductors

Proton conduction in acceptor-doped perovskites is fundamentally a vibronic process: mobile H + and D + do not move independently, but dynamically co-vibrate with the surrounding oxygen–metal framework. Direct experimental evidence for this behavior is presented using in situ 119⁢ 𝑆⁢𝑛 nuclear resonance vibrational spectroscopy (NRVS) on hydrated, deuterated, and dry 𝐵⁢𝑎⁢𝑆⁢𝑛 1−𝑥⁢ 𝑌 𝑥 ⁢𝑂 3−𝛿 . Hydration induces systematic redistributions in the Sn-projected phonon density of states (PDOS), including an upshift of the first spectral moment by about 0.4 meV, indicating a stiffening of the extended Sn–O network. H/D isotopic substitution leaves the Sn-projected PDOS largely unchanged, with only subtle isotope-dependent spectral reweighting, demonstrating that protonic degrees of freedom are not localized oscillators but are embedded in collective lattice modes. These results are rationalized using a classical coupled proton–phonon oscillator model that links the observed PDOS variations to changes in effective force constants and vibrational mass terms. The model captures how H + and D + participate in cooperative lattice dynamics rather than forming isolated OH/OD entities. Overall, NRVS probes proton–lattice coupling in ceramic proton conductors and quantitatively describes how protonic defects modulate host lattice dynamics to enable phonon-assisted long-range proton transport.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ion dynamics in hexagonal boron nitride ionogel electrolytes

Ionogel electrolytes incorporating exfoliated hexagonal boron nitride (hBN) nanoplatelets are promising materials for next-generation energy storage systems. However, detailed understanding of their ion transport properties at the molecular level remains limited. This study employs diffusion and relaxation nuclear magnetic resonance (NMR) techniques, including fast-field cycling (FFC) NMR, to investigate the dynamics of ionic species in hBN-ionogels. By spanning a broad frequency range from 30 kHz using FFC NMR to high-field NMR (500–800 MHz), we reveal distinct relaxation mechanisms governing ion dynamics in ionogels with and without lithium salts. Our results highlight the role of hBN in modulating molecular rotation and translational motion, significantly affecting 1 H and 19 F relaxation profiles. The presence of Li + alters the dynamic behavior in ionogels, enhancing anion mobility at the interface. Notably, 7 Li relaxation reveals strong interactions with the hBN surface that cannot be detected by diffusion NMR. Furthermore, these findings underscore the importance of spanning a broad frequency range in NMR studies of ionogels and provide critical insights into optimizing their design as novel electrolytes.

Electrolytes↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2024

Development of a coarse-grained molecular dynamics model for poly(dimethyl- co -diphenyl)siloxane

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. Here, to overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2026

How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics

Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar water splitting and nanoporous membranes. In this work, we develop a machine learning interatomic potential, trained via active learning, to achieve ab initio accuracy for water confined between hydroxylated silica surfaces over a range of silanol coverages and slit widths. We find that partially hydroxylated surfaces (50 and 75% OH) support stronger water−surface hydrogen bonding and more extended interfacial density profiles than fully hydroxylated (100% OH) surfaces, indicating that increasing OH coverage does not necessarily strengthen interfacial hydrogenbond networks. Translational diffusion decreases approximately linearly with slit width and OH coverage, whereas rotational dynamics respond nonlinearly. In particular, at the smallest slit width of 5 Å, 75% OH coverage produces an enhanced local tetrahedral ordered interfacial network that strongly suppresses reorientation, while 100% coverage yields a crowded, disordered interfacial layer that also hinders rotation. In contrast, the 50% OH coverage is sufficiently sparse that it does not markedly alter water structure or dynamics under confinement. These results show that coupled control of pore size and surface chemistry enables nonlinear tuning of interfacial water structure and transport, providing a design strategy for optimizing porous silica for either enhanced interfacial stability and controlled reactivity or rapid and selective transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Injection Locking Dynamics of Oscillation Loop with Saturable Gain

We analytically derive and experimentally verified the injection-locking range and phase-noise suppression ratio of an oscillation loop with saturable gain, establishing a general and straightforward approach for analyzing injection locking dynamics.

Xi, Zichen [ORNL]↗
DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗
DOE OSTI2024

Insights into the Properties of MXenes and MXene Analogs from Atomistic Simulation

We review the properties that have been predicted for MXenes and MXene analogs from computational simulation with a focus on structural and electronic properties, energy storage, and ion transport. Methods considered range from quantum mechanical approaches to classical molecular dynamics. We conclude by reviewing current limitations and outstanding questions for simulation and MXene properties that have been little explored to-date.

Muraleedharan, Murali Gopal↗
DOE OSTI2025

Unravelling Microstructure Selection in an Additively Manufactured Eutectic High‐Entropy Alloy

High-entropy alloys (HEAs) are promising candidates for advanced structural applications due to their excellent mechanical properties. Additive manufacturing (AM), with its rapid solidification conditions, enables the creation of unique nonequilibrium microstructures. To fully leverage the synergy between AM and HEAs, understanding how processing affects structure and properties is essential. Here, how solidification rate influences microstructure evolution and phase transformation pathway in laser additively manufactured AlCrFe2Ni2 eutectic HEAs is investigated. By increasing the laser scan speed and hence the solidification rate, distinct solidification modes evolving from coupled eutectic to anomalous eutectic and eventually to single-phase solidification are revealed. These transitions result in distinct microstructures and a wide range of mechanical properties. Thermodynamic modeling and molecular dynamics simulations reveal that low cooling rates allow for sufficient atomic diffusion and phase separation, facilitating coupled eutectic growth. In contrast, rapid cooling suppresses diffusion and destabilizes the solid–liquid interface, promoting anomalous or single-phase solidification. This integrated experimental and computational approach provides a multiscale understanding of solidification mechanisms in HEAs and underscores how kinetic effects can over-ride thermodynamic predictions under nonequilibrium conditions. Furthermore, these results demonstrate that AM can serve as a powerful tool to design HEAs with tailored microstructures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Zooplankton International Geospatial dataset: A global repository of spatiotemporal freshwater zooplankton community composition data from lakes and reservoirs to support ecological research

Zooplankton transfer substantial energy in aquatic food webs and are used as indicators of environmental change. Syntheses of zooplankton community dynamics globally require datasets that span a wide range of environmental gradients; however, these datasets are limited due to methodological differences across programs, taxonomic inconsistencies, and a lack of standardized metadata. To reconcile these challenges, we created the Zooplankton International Geospatial (ZIG) dataset, which includes original zooplankton, water physical and chemical variables, and lake morphometric data from 311 inland lakes and reservoirs. ZIG includes waterbodies ranging in size from 0.005 to 82,100 km2 and spanning broad latitudinal (−47.26 to 64.90) and longitudinal ranges (−165.04 to 176.53). Temporal coverage for individual waterbodies ranges between 1 and 60 yr with sampling frequency ranging from annually to weekly. With its extensive coverage and content, we consider ZIG to be a cornerstone for future investigations of global scale lake biodiversity change.

Figary, Stephanie [Cornell University, Ithaca, NY]↗
DOE OSTI2026

Adaptation of virtual synchronous generators to dynamic conditions in power grids

Virtual synchronous generators (VSGs) are widely adopted as grid-forming controls for inverter-based resources. However, when grid conditions vary significantly as characterized by changes in short-circuit ratio (SCR) and the reactance-to-resistance (X/R) ratio, fixed-gain designs and the commonly used P–Q decoupling assumption can become inaccurate. Such conditions can degrade transient power performance, leading to oscillations, prolonged settling, and overshoot, particularly in stiff-grid operating points. This paper quantifies how grid strength and impedance-dependent coupling affect the active–reactive power dynamics of a conventional VSG over a broad range of SCR and X/R values. An adaptive VSG tuning framework is then developed by combining (i) a coupling-explicit, impedance-parameterized state-space model to enable systematic controller synthesis, (ii) a full-state-feedback law designed via pole placement to meet prescribed damping and settling-time specifications, and (iii) a physics-informed neural network (PINN)–based online grid-impedance estimator that updates controller gains in real time as grid conditions vary. Offline simulations in MATLAB/Simulink and real-time validation on an OPAL-RT platform show that the proposed method preserves consistent damping and settling behavior with reduced overshoot across wide SCR and X/R ranges, compared with fixed-gain VSG baselines.

Adaptive control↗
DOE OSTI2025

Ultra-high efficiency hydrogen production using a large-scale solid oxide electrolysis cell system

Efficient and cost-effective production of clean hydrogen is key to decarbonizing the production of hard-to-abate industries, such as chemicals, fuels, steel, cement and many other commodities that form the basis of modern societies. High-temperature steam electrolysis (HTSE) has recently become commercially available and offers opportunities for producing hydrogen at higher efficiency and lower cost than competing low temperature technologies. In this work, we report world record setting hydrogen production efficiency from large-scale prototype HTSE systems based on solid oxide electrolysis cell (SOEC) technology. Independent tests performed at Idaho National Laboratory (INL) employed a Bloom Energy 100 kW SOEC system to achieve a hydrogen production direct current specific electric energy consumption as low as 36.7 kWh per kilogram of hydrogen. Remarkably, similar high efficiencies in the range of 36–39 kW/kg-H2 were obtained over a wide range of hydrogen production rates and even during dynamic ramping as the hydrogen production and electric power consumption of the system were varied between 20 % and 100 % of nominal conditions. Furthermore, these test results validate previous projections that commercial SOEC systems can produce clean hydrogen at efficiencies approaching 100 % for less than 2 U S. dollars per kilogram when located near sources of inexpensive, low-grade heat and clean electricity.

08 HYDROGEN↗
DOE OSTI2024

SAXS of murine amelogenin identifies a persistent dimeric species from pH 5.0 to 8.0

Amelogenin is an intrinsically disordered protein essential to tooth enamel formation in mammals. Here, using advanced small angle X-ray scattering (SAXS) capabilities at synchrotrons and computational models, we revisited measuring the quaternary structure of murine amelogenin as a function of pH and phosphorylation at serine-16. The SAXS data shows that at the pH extremes, amelogenin exists as an extended monomer at pH 3.0 (R g = 38.4 Å) and nanospheres at pH 8.0 (R g = 84.0 Å), consistent with multiple previous observations. At pH 5.0 and above there was no evidence for a significant population of monomeric species. Instead, at pH 5.0 ~ 80% of the population is a heterogenous dimeric species that increases to ~ 100% at pH 5.5. The dimer population was observed at all pH > 5 conditions in dynamic equilibrium with a species in the pentamer range at pH < 6.5 and nanospheres at pH 8.0. At pH 8.0 ~ 40% of the amelogenin still remained in the dimeric state. In general, serine-16 phosphorylation of amelogenin appears to modestly stabilize the population of the dimeric species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Magneto-optical spectroscopy based on pump-probe strobe light

Here, we demonstrate a pump-probe strobe light spectroscopy for sensitive detection of magneto-optical dynamics in the context of hybrid magnonics. The technique uses a combinatorial microwave-optical pump-probe scheme, leveraging both the high-energy resolution of microwaves and high-efficiency detection using optical photons. In contrast with conventional stroboscopy using continuous-wave light, we apply microwave and optical pulses with varying pulse widths, and demonstrate magneto-optical detection of magnetization dynamics in Y 3 Fe 5 O 12 films. The detected magneto-optical signals depend strongly on the characteristics of both the microwave and the optical pulses, as well as their relative time delays. We show that good magneto-optical sensitivity and coherent stroboscopic character are maintained, even at a microwave pump pulse of 1.5 ns and an optical probe pulse of 80 ps, under a 7 MHz clock rate, corresponding to a pump-probe footprint of approximately 1% in one detection cycle. Our results show that time-dependent strobe light measurement of magnetization dynamics can be achieved in the gigahertz frequency range under a pump-probe detection scheme.

36 MATERIALS SCIENCE↗