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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗
DOE OSTI2024

Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO 2 Conversion Reagents in Metal–Organic Frameworks

Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO 2 cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO 2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. Here we verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.

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

Measurement of distribution coefficients on TODGA resin and development of a group separation for mixed activation and fission product samples

Batch equilibrium experiments were performed to determine the distribution coefficients for Sc, Ir, and Ta on N,N,N’,N’tetraoctyl-1,5-diglycolamide (TODGA) resin in various acid matrices. Distribution coefficients were determined in hydrochloric acid (Sc, Ir), nitric acid (Sc, Ta), and a mixture of nitric acid and 0.2M hydrofluoric acid (Sc, Ir, Ta). The distribution coefficients determined through these experiments were then utilized to create a group separation method for activation products, fission products, and environmental constituents.

Louie, Christina A.↗
DOE OSTI2026

Effect of Solvent on the Local Structure, Dynamics, and Vibrational Density of States in Sn-BEA Zeolite

Lewis acid zeolites are attractive catalysts for epoxidation and biomass valorization, as they are highly active and selective in the liquid phase and can operate at or near ambient conditions. While a rich experimental literature exists on liquid-phase Lewis acid zeolite catalysis, our understanding of the molecular organization and solvent dynamics in the vicinity of Lewis acid sites with differing metal site speciation remains limited. In this work, we investigate the molecular coordination and diffusion of two common solvents (methanol and water) around the closed and open Sn-BEA zeolite active sites using molecular dynamics simulations with a machine-learned interatomic potential trained on ab initio molecular dynamics trajectories. Molecular dynamics simulations reveal that introducing active sites significantly enhances local order in the first and second solvation shells compared to the pure silica case. For methanol, both closed and open active sites are singly coordinated, while more than two water molecules coordinate the open site. In contrast to methanol, we observed that water molecules dissociate, leading to the formation of additional Sn-OH and silanol groups away from the active site. The diffusion coefficients of water and methanol are functions of the solvent population in the pore. Here, our work provides insights into how active site speciation in Lewis acid zeolites affects solvent coordination, diffusion, and vibrational signature. This information is foundational for catalyst design and optimization of liquid-phase catalytic processes in zeolites. It also demonstrates the suitability of machine-learned interatomic potentials for modeling reactive systems, enabling sufficiently long trajectories for appropriate statistical averaging.

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

Time reversal symmetry breaking and odd viscosity in active fluids: Green–Kubo and NEMD results

Active fluids, which are driven at the microscale by non-conservative forces, are known to exhibit novel transport phenomena due to the breaking of time reversal symmetry. Recently, Epstein and Mandadapu [arXiv:1907.10041 (2019)] obtained Green-Kubo relations for the full set of viscous coefficients governing isotropic chiral active fluids, including the so-called odd viscosity, invoking Onsager's regression hypothesis for the decay of fluctuations in active non-equilibrium steady states. In this Communication, we test these Green-Kubo relations using molecular dynamics simulations of a canonical model system consisting of actively torqued dumbbells. We find the resulting odd and shear viscosity values from the Green-Kubo relations to be in good agreement with values measured independently through non-equilibrium molecular dynamics flow simulations. This provides a test of the Green-Kubo relations and lends support to the application of the Onsager regression hypothesis in relation to viscous behaviors of active matter systems.

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

Modeling Europium (II/III) ion solvation in the LiCl-KCl eutectic mixture with polarizable force fields

Here, the solvation processes of Europium (II/III) ions within the molten salt eutectic mixture, 3LiCl-2KCl, are investigated over temperatures ranging from 673 K to 1173 K. New polarizable ion force fields are proposed to model Europium ions in a molten salt eutectic mixture with a goal of accurately capturing the underlying physics in the solutions. In contrast to rigid-ion models, the polarizable Drude model with an adjusted WBK (Wang-Buckingham) force field significantly improves predictions for diffusion coefficients, the average diffusion activation energy, and changes in excess ion chemical potential and partial molar entropy, producing good agreement with experimental measurements.

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

Interfacial Hydrogen-Bond Dynamics in Transition Metal Compounds

Understanding how water behaves when confined within atomic layers of active transition-metal carbides, nitrides, and carbonitrides is essential for uncovering the fundamental principles needed to engineer solid–liquid interfaces at the atomic scale. Yet, how lattice element chemistry and surface termination groups collectively regulate the structure and mobility of such interlayer water remains poorly understood. Here, we present a composition-controlled investigation of interlayer water dynamics in layered transition-metal nitride, carbide, and carbonitride systems using a systematic integration of quasi-elastic neutron scattering (QENS), ab initio molecular dynamics (AIMD) simulations, and density functional theory (DFT) calculations. QENS measurements show that nitride-rich systems host mobile, translationally diffusing water with thermally activated self-diffusion coefficients on the order of 10 –10 m 2 s –1 , whereas mixed C/N lattices confine water to localized, nontranslational motion that is insensitive to temperature. AIMD and DFT reveal that lattice C/N chemistry and surface functional group composition reshape the first hydration layer by modulating the surface electronic structure and termination-dependent hydrogen-bond networks, leading to pronounced differences in water ordering and thermal resilience. On the other hand, fully carbide systems exhibit intermediate behavior, highlighting that water mobility is not primarily controlled by the hydration level alone but by the coupling between lattice composition and surface chemistry. Overall, this study establishes how surface chemistry and lattice composition jointly control interfacial hydrogen bond dynamics, offering a mechanistic framework for designing transition-metal layered materials with tailored interfacial transport properties.

Hydration↗
DOE OSTI2025

Developing the Science Basis for Understanding Polymer Encapsulant Degradation Mechanisms: DuraMAT 2.0 Final Project Report

Polymeric encapsulants are essential materials in photovoltaic modules, protecting sensitive electronics from the environment while providing mechanical integrity to the multilayered assembly. However, these polymeric materials are susceptible to degradation processes driven by the ingress of environmental species, ultraviolet radiation, thermal stresses, and mechanical loading. In this study, we employ a combined atomistic simulation and accelerated aging experimental approach to study the molecular-scale mechanisms of encapsulant degradation. Classical molecular dynamics simulations quantify the diffusion of environmental and degradation species through the polymer matrix, producing composition-specific diffusion coefficients. Reactive simulations characterize activation energy barriers and reaction rate constants for key chemical pathways. In parallel, thermal-desorption analyses coupled with mass spectrometry monitor the emergence and concentration profiles of degradation products under controlled stressor conditions. By integrating simulation and experiment, we establish quantitative correlations between polymer composition, species diffusivity, and chemical reactivity. We anticipate that these relations and quantitative values could serve as high-fidelity inputs to reaction-diffusion models, enabling physics-informed lifetime predictions and guiding the design of more durable encapsulant materials for solar energy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nucleation rate controlled grain boundary and lattice creep

Nucleation versus diffusion rate-limited bicrystal and single crystal creep exhibit different scaling dependencies that enable the mechanisms to be isolated when measured as a function of sample size. It has recently been suggested that nucleation rate-limited kinetic models generally describe the non-Newtonian portion of the creep response well, but more direct evidence is required. This work analyzes the grain boundary creep response of UO 2 , a pyrochlore high entropy oxide, silver, and palladium, along with the lattice creep of silver using small-scale in situ loading in the transmission electron microscope. At small sizes, each system exhibits scale dependence associated with nucleation rate-limited kinetics. Fits of the data produce activation volumes on the order of a few Burgers vectors cubed with positive temperature coefficients as expected for nucleation kinetics. The activation enthalpies fall in the range of about 0.4 eV to 1.7 eV, being lower for the metals and higher for the oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tritium diffusion and formation in the bulk and defective surface of γ-LiAlO2 pellets: First-principles investigation

In tritium-producing burnable absorber rods (TPBAR), γ-LiAlO2 is used in the form of an annular ceramic pellet enriched with the 6Li isotope. When irradiated in a pressurized water reactor (PWR), the <sup>6</sup>Li pellets absorb neutrons and produce tritium (<sup>3</sup>H) through <sup>6</sup>Li + n <sup>3</sup>H + α. The 3H chemically reacts with the metal getter where it is captured and leads to formation of a metal hydride. For TPBARs to enable effective tritium production in PWRs, we investigated the <sup>3</sup>H diffusion pathways in the bulk and surface of γ-LiAlO<sub>2</sub> with different concentrations of lithium defects. The calculated results for bulk and low-index surfaces, thermal conductivity, 3H activation energy barriers, and the 3H diffusion coefficients in γ-LiAlO<sub>2</sub> are in good agreement with the available experimental values. In the bulk, our results show that the smallest activation energy barrier is 0.63 eV for substitutional <sup>3</sup>H diffusion with a diffusion coefficient of 3.25x10<sup>-12</sup> m<sup>2</sup>/s. After <sup>3</sup>H diffused from bulk to the surface, it could form different species (such as <sup>3</sup>H<sub>2</sub>, <sup>3</sup>H<sub>2</sub>O, C<sup>3</sup>H<sub>4</sub>) depending on the surface structure, vacancy types and the impurity carbon. Our results indicate that the <sup>3</sup>H<sub>2</sub> is the main product from γ-LiAlO<sub>2</sub> pellets. As the number of V<sub>Li</sub> vacancies and <sup>3</sup>H atoms increases under irradiation (<sup>3</sup>H-rich condition), <sup>3</sup>H<sub>2</sub>O release could increase from the surface of γ-LiAlO<sub>2</sub> pellets.

Jia, Ting↗
DOE OSTI2023

Inner Belt Electron Decay Timescales: A Comparison of Van Allen Probes and DREAM3D Losses Following the June 2015 Storm

Abstract NASA's Van Allen Probes observed significant, long‐lived fluxes of inner belt electrons up to ∼1 MeV after geomagnetic storms in March and June 2015. Reanalysis of Magnetic Electron Ion Spectrometer (MagEIS) data with improved background correction showed a clearer picture of the relativistic electron population that persisted through 2016 and into 2017 above the Fennell et al. (2015, https://doi.org/10.1002/2014gl062874 ) limit. The intensity and duration of these enhancements allow estimation of decay timescales for comparison with simulated decay rates and theoretical lifetimes. We compare decay timescales from these data and DREAM3D simulations based on them using geomagnetic activity‐dependent pitch angle diffusion coefficients derived from plasmapause‐indexed wave data (Malaspina et al., 2016, https://doi.org/10.1002/2016gl069982 , 2018, https://doi.org/10.1029/2018gl078564 ) and phase space densities derived from MagEIS observations. Simulated decay rates match observed decay rates more closely than the theoretical lifetime due to significantly nonequilibrium pitch angle distributions in simulation and data. We conclude that nonequilibrium effects, rather than a missing diffusion or loss process, account for observed short decay rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Evaluation of Fe-βGa 2 O 3 for Photoconductive Semiconductor Switching

We present iron-doped beta gallium oxide (Fe- β Ga2O3) as a candidate for photoconductive semiconductor switches (PCSSs) with sub-bandgap light. From a commercially available Fe- β Ga2O3 wafer, we first did material characterization. This included measurements of absorption coefficient and dopant composition, carrier activation energy up to 200 °C, break down field of planar electrodes (limited from material passivation), and free carrier recombination lifetime, and thermal effects up to 203 °C on photocurrent with a 447 nm light emitting diode (LED) source. Here, we then demonstrated pulsed operation of a Fe- β Ga2O3 PCSS under different sub-bandgap wavelengths (355, 532, and 1064 nm) and sub-ns pulses. Fe- β Ga2O3 is a candidate for high temperature PCSS with 355 nm responsivity of 7×10 -7 A-cm/W-kV at room temperature and up to 5.5×10 -4 A-cm/W-kV at 200 °C. From these investigations, we discuss a simple trap model to describe the illumination process of the PCSS. Fe- β Ga2O3 has a high breakdown field and has moderate responsivity characteristics, but the dark current at high temperature leads to low photo-to-dark current ratio (PDCR). Regardless, we verify its potential as a PCSS material for harsh environment applications.

42 ENGINEERING↗
DOE OSTI2021

Galvanostatic Intermittent Titration Technique Reinvented: Part II. Experiments

Following a critical review of the galvanostatic intermittent titration technique in Part I, here we experimentally demonstrate how to extract chemical diffusivity with a modified method. We prepare dense bulk samples that ensure diffusion-limitation. We utilize the scaling with $\sqrt{{t}_{\mathrm{relax}}+\tau }-\sqrt{{t}_{\mathrm{relax}}}$ (trelax: relaxation time; τ: pulse duration), avoiding problems with composition-dependent overpotentials. The equilibrium Nernst voltage is measured separately using small porous particles. This separation between the diffusion measurement and the titration procedure is critical for performing each measurement in a reliable setting. We report the chemical diffusion coefficients of LixNi1/3Mn1/3Co1/3O2 and their activation energy. We extract ionic conductivity and compare it with total conductivity to confirm ion-limitation in chemical diffusion. The measurements suggest that the time scale for diffusion in typical Li-ion battery particles could be much shorter than that of the intercalation/deintercalation processes at the particle surface (Biot number less than 0.1).

25 ENERGY STORAGE↗
DOE OSTI2024

Radionuclide-specific Parameters Dataset

The radionuclide-specific parameters dataset is searchable for radiological information for multiple isotopes simultaneously. After selecting radionuclides of interest and the desired parameters, the RAIS will generate a table containing the values, chosen according to an established hierarchy. Results can be downloaded in Excel format. 50 parameters are available, including atomic number, soil to animal transfer coefficients, plant uptake coefficients, half-life, specific activity, and water solubility. Seven primary sources are used to populate the dataset of radiological-specific parameters. These values should be used in cancer risk assessments for the calculation of preliminary remediation goals (PRGs), hazard characterization, and transport modeling. Users can select up to 1000 radionuclides per query. The dataset supports environmental risk assessments, regulatory decision-making, and environmental planning with tools for benchmarking against risk-based standards. This structured approach ensures a robust evaluation of environmental risks tailored to regulatory needs.

Manning, Karessa [Oak Ridge National Laboratory (O↗
DOE OSTI2023

Physicochemical characterization of free troposphere and marine boundary layer ice-nucleating particles collected by aircraft in the eastern North Atlantic

Abstract. Atmospheric ice nucleation impacts the hydrological cycle and climate by modifying the radiative properties of clouds. To improve our predictive understanding of ice formation, ambient ice-nucleating particles (INPs) need to be collected and characterized. Measurements of INPs at lower latitudes in a remote marine region are scarce. The Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign, in the region of the Azores islands, provided the opportunity to collect particles in the marine boundary layer (MBL) and free troposphere (FT) by aircraft during the campaign's summer and winter intensive operation period. The particle population in samples collected was examined by scanning transmission X-ray microscopy with near-edge X-ray absorption fine structure spectroscopy. The identified INPs were analyzed by scanning electron microscopy with energy-dispersive X-ray analysis. We observed differences in the particle population characteristics in terms of particle diversity, mixing state, and organic volume fraction between seasons, mostly due to dry intrusion events during winter, as well as between the sampling locations of the MBL and FT. These differences are also reflected in the temperature and humidity conditions under which water uptake, immersion freezing (IMF), and deposition ice nucleation (DIN) proceed. Identified INPs reflect typical particle types within the particle population on the samples and include sea salt, sea salt with sulfates, and mineral dust, all associated with organic matter, as well as carbonaceous particles. IMF and DIN kinetics are analyzed with respect to heterogeneous ice nucleation rate coefficients, Jhet, and ice nucleation active site density, ns, as a function of the water criterion Δaw. DIN is also analyzed in terms of contact angles following classical nucleation theory. Derived MBL IMF kinetics agree with previous ACE-ENA ground-site INP measurements. FT particle samples show greater ice nucleation propensity compared to MBL particle samples. This study emphasizes that the types of INPs can vary seasonally and with altitude depending on sampling location, thereby showing different ice nucleation propensities, which is crucial information when representing mixed-phase cloud and cirrus cloud microphysics in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evaluating the limitations of Bayesian metabolic control analysis

Bayesian Metabolic Control Analysis (BMCA) is a promising framework for inferring metabolic control coefficients in data-limited scenarios, combining Bayesian inference with linear-logarithmic (lin-log) rate laws. These metabolic control coefficients quantify how changes in enzyme activities affect steady-state fluxes and metabolite concentrations across a metabolic network. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCC), and concentration control coefficients (CCC) under varying data availability conditions using three synthetic metabolic network models. We demonstrate that BMCA predictions are highly dependent on the inclusion of flux and enzyme concentration data, with the omission of these datasets leading to severe inaccuracies. In our synthetic, enzyme-perturbation datasets, external metabolite concentrations had minimal impact and, in some cases, their exclusion improved predictions; when external-nutrient perturbations were introduced and those concentrations were observed, gains were at most modest. Additionally, we find that posterior estimation with both ADVI and HMC can underestimate large-magnitude elasticities in our synthetic settings, with ADVI showing somewhat higher variance under strong up-regulation; thus, recovering |elasticity| ≳ 1.5 remains challenging regardless of the inference engine. ADVI also fails to accurately infer allosteric interactions, even when regulatory effects are strong. While BMCA maintains reasonable accuracy in partially recovering the rankings of the highest FCC values, its estimates of absolute values remain constrained by prior assumptions and data limitations. Our findings reveal the BMCA algorithm’s strengths and weaknesses, providing guidance on its application in metabolic engineering, and highlighting the need for methodological refinements to enhance its predictive capabilities.

59 BASIC BIOLOGICAL SCIENCES↗