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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Temperature dependence of heavy quark diffusion from (2+1)-flavor lattice QCD

We present a lattice determination of the heavy-quark diffusion coefficient in (2+1)-flavor QCD with almost physical quark masses. The momentum and spatial diffusion coefficients are extracted for a wide temperature range, from T = 163 MeV to 10 GeV. The results are in agreement with previous works from the HotQCD collaboration, and show fast thermalization of the heavy quark inside the QGP. Near the chiral crossover temperature T c ≃ 150 MeV, our results are close to the AdS/CFT estimation computed at strong coupling.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Impact of cement composition, brine concentration, diffusion rate, reaction rate and boundary condition on self-sealing predictions for cement-CO 2 systems

Geological CO 2 storage (GCS) plays an important role in curbing CO 2 emissions by reducing the carbon footprint of difficult to decarbonize operations and achieve negative CO 2 emissions through activities like Bioenergy with Carbon Capture and Storage (BECCS) and Direct Air Carbon Capture and Storage (DACCS). Leakage of CO 2 through wells is an important concern when it comes to deployment of large-scale GCS. Existing wells in sites that are otherwise suitable for GCS can act as conduits for stored CO 2 to escape the reservoir. There is broad consensus that the main risk of leakage through wellbores is via fractures/damaged pathways. The results from several studies evaluating the permeability evolution of cement fractures in wells upon leakage of CO 2 agree that smaller fracture apertures, slower brine velocities and higher brine residence times promote self-sealing of fractures by mineral precipitation. Quantitatively, however, the differences in sealing conditions are significant and are typically attributed to differences in experimental conditions or model assumptions. Here we examine the sensitivity of our model, describing CO 2 leakage through wellbores, to cement composition, brine concentration, diffusion rates, and reaction rates. We also evaluate the impact of the boundary condition to allow comparisons between observations from experiments performed at constant flow rate and model predictions made at constant pressure conditions. Our results show that diffusion and reactions rates have the most impact on the self-sealing criteria for cement-CO 2 systems. In addition, conditions associated with self-sealing of fractures at constant flow rate require longer fractures, smaller fracture apertures, and slower velocities than under constant pressure.

58 GEOSCIENCES↗
DOE OSTI2025

Understanding the impact of the gas diffusion layer structure on catalyst utilization in the PEM water electrolyzer

A multiphysics half-cell model of a polymer electrolyte membrane water electrolyzer (PEMWE) was developed to probe impacts of the detailed 3-dimensional pore structure of the gas diffusion layer (GDL) on performance characteristics. We show that pores in the titanium GDL mesh led to significant underutilization of the catalyst layer (CL), with only 45% of the catalyst effectively utilized. This contradicts the assumption of uniform electron flow across the CL, as shown in graphical abstract (a), as near-zero current was observed near GDL pore regions and the current distribution in CL was influenced by GDL structure, as shown in graphical abstract (b). Instead, oxygen generation was primarily concentrated under the solid titanium regions, diffusing out around the pore walls. High current density peaks were also noted at the GDL-catalyst contact, correlating with degradation hotspots that were directly observed in companion experiments, as shown in graphical abstract (c). Collectively, these findings point to the critical importance of the heterogeneous GDL porous architecture not only for PEMWE efficiency but also for uneven degradation of the CL.

COMSOL↗
DOE OSTI2024

Thermal diffusion, exhaust gas recirculation and blending effects on lean premixed hydrogen flames

Thermodiffusively-unstable lean premixed hydrogen flames are investigated using two-dimensional direct numerical simulation employing finite-rate chemical kinetics. Three databases are generated focussing on the inclusion of the Soret effect, the recirculation of exhaust gas, and blending with methane. A simple rescaling of a classic thermal diffusion model is presented and shown to mimic multicomponent diffusion with very low computational cost and little-to-no loss in accuracy. It is also shown that a previously developed model for mean local flame speeds in lean premixed hydrogen flames can still be used provided Soret effects are taken into account in one-dimensional calculations. The addition of exhaust gas to the unburned mixture is found to enhance thermodiffusive instability; the primary mechanism for this was shown to be the highly-efficient third-body nature of water, with the reduction of adiabatic flame temperature a second-order effect. Again, the existing mean local flame speed model proved sufficient. Finally, blending with methane was found to reduce the thermodiffusive response of the flame, more so than the existing model suggests, despite adjustment of the fuel Lewis number; an adapted model is presented to account for this.

08 HYDROGEN↗
DOE OSTI2024

Enhanced passivation and stability of negative charge injected SiN x with higher nitrogen content on the boron diffused surface of n-type Si solar cells

This paper explores the potential of the negatively charged SiN x using plasma charge injection technology to passivate the front textured boron-diffused emitter of n-type Si solar cells. The high-x value single SiN x layer with x ≥ 1.30 (x = N/Si) previously developed for the planarized rear-side passivation of p-type silicon solar cells, with an excellent passivation and charge stability, was found to be unacceptable for the passivation on the front textured boron emitter of n-type cells due to a severe bulk lifetime degradation issue. Therefore, in this study, we investigated a new concept of depositing a dual-x SiN x with a low-x SiN x layer (x~1.01) on top of a high-x SiN x layer (x~1.30) for passivation of the front textured rough surface of boron diffused emitter in n-type cells. Here, the optimized dual-x SiN x stack reveals the promise of charge retention for more than 25 years in field operation as well as excellent passivation of boron-doped emitter without bulk lifetime degradation. N-type cells with the optimized dual-x SiN x after charge injection show comparable cell performance to Al 2 O 3 passivated reference cells. The results of cell-level light stability tests using one-cell minimodules exhibit cell performance decay characteristics similar to the traditional Al 2 O 3 /SiN x passivated reference module.

14 SOLAR ENERGY↗
DOE OSTI2025

Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide

Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.

Actinide↗
DOE OSTI2026

Breaking the Energy Barrier of Heavy Metal Ion Diffusion in Micropores with Mesoporous 3D Graphene for Fast and Efficient Cu2+ Removal

Efficient removal of heavy metals from water critically depends not only on adsorption capacity but also on ion diffusion kinetics and the associated energy barriers. In conventional carbon adsorbents, severe diffusion confinement within micropores restricts ion transport, resulting in sluggish adsorption kinetics and large apparent activation energies despite high specific surface areas. Here, we demonstrate that this fundamental limitation is overcome by engineering meso/macroporous architectures in the 3D graphene materials synthesized via our discovered alkali-metal reactions with\\\\r\\\\n2\\\\r\\\\nCO. The unique 3D graphene materials possess defect-rich graphene frameworks with interconnected meso/macroporous networks, exhibiting simultaneously high surface area and greatly enhanced meso/macropore volume that enable efficient access to adsorption sites. As a result, the Cu2+ adsorption on 3D graphene proceeds with very low activation energies (4.98 kJ mol–1), which is almost 4 times smaller than on activated carbon (23.1 kJ mol–1). This finding offers a promising platform for efficient and sustainable water purification.

25 ENERGY STORAGE↗
DOE OSTI2024

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics

The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Carrier diffusion in long wavelength InGaN quantum well LEDs after injection through V-defects

The efficiency of operation of GaN-based light emitting diodes (LEDs) to a large degree relies on realization of a uniform hole distribution between multiple quantum wells (QWs) of the active region. Since the direct thermionic transport between the QWs is inefficient, the hole injection through semipolar {$10\bar{1}1$} QWs that form on the facets of V-defects has been suggested as an alternative approach. However, for an efficient LED operation, the carrier distribution should be uniform not only vertically, between the QWs but also laterally, within individual QWs. In this work, the lateral carrier distribution in long wavelength InGaN/GaN QW LEDs is studied by the scanning near-field optical microscopy. The measurements have shown that emission is concentrated around the V-defect injectors. At high currents, the diffusion length of holes in polar QWs was found to be ∼0.6–1 μm and the hole diffusion coefficient ∼0.6 cm 2 /s. The obtained data should aid design of the V-defect injectors for a laterally uniform carrier distribution in the active region QWs.

Current crowding↗
DOE OSTI2024

RASPA3

RASPA3, a molecular simulation code for computing adsorption and diffusion in nanoporous materials and thermodynamic and transport properties of fluids. It implements force field based classical Monte Carlo/molecular dynamics in various ensembles. RASPA3 is rewritten from the ground up in C++23 with speed and code readability in mind. Transition-matrix Monte Carlo is added to compute the density of states and free energies. The Monte Carlo code for rigid molecules is based on quaternions, and the atomic positions needed in the energy evaluation are recreated from the center of mass position and quaternion orientation. The expanded ensemble methodology for fractional molecules, with a scaling parameter λ between 0 and 1, now also keeps track of analytic expressions of dU/dλ, allowing independent verification of the chemical potential using thermodynamic integration. The source code is freely available under the MIT license on GitHub.

Dubbeldam, David↗
DOE OSTI2025

Fast lithium ion diffusion in brownmillerite Li x Sr 2 Co 2 O 5

Transition metal oxides not only exhibits novel magnetic properties but also provides outstanding ionic transports. Ionic conductors have great potential for interesting tunable physical properties via ionic liquid gating and novel energy storage applications such as all-solid-state lithium batteries. In particular, low migration barriers and high hopping attempt frequency are the keys to achieve fast ion diffusion in solids. Taking advantage of the oxygen-vacancy channel in Li x Sr 2 Co 2 O 5 , we show that migration barriers of lithium ion are as small as 0.28–0.17 eV depending on the lithium concentration rates. Our first-principles calculation also investigated hopping attempt frequency and concluded the room temperature ionic diffusivity and ion conductivity are high as 10 −7 –10 −6 cm 2 s −1 and 10 −3 –10 −2 Scm −1 , respectively, which outperform most of perovskite-type, garnet-type, and sulfide Li-ion solid-state electrolytes. This work proves Li x Sr 2 Co 2 O 5 as a promising super-ionic conductor.

Crystallographic defects↗
DOE OSTI2025

Consistent solutions of the radiation diffusion equation in spherical and cylindrical geometries

We have extended the radiation diffusion model of Hammer and Rosen [Phys. Plasmas 10, 1829 (2003)] to diverging spherical and cylindrical geometries. The effect of curvilinear geometry on the supersonic, expanding wavefront increases as the internal radius of a spherical or cylindrical shell approaches zero. Small spherical geometries are important for modeling systems at the size scale of inertial confinement fusion capsules, at these scales existing quasi-analytic models for planar geometry significantly disagree with the results of simulation. With this method, the benefits of rapid iteration can be applied to common spherical systems at much smaller length scales. We present comparisons between numerical diffusion solutions and the analytic model to give ranges of applicability for the model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Suppressing proximity effects during rapid serial two-photon lithography through tuning of reaction–diffusion kinetics

The ability of two-photon lithography (TPL) to print deterministic nanoporous 3D structures is highly valuable for many applications. However, it is challenging to print such structures rapidly due to the proximity effects that cause closely spaced features to enlarge and merge. A key challenge is the limited understanding of the origins and spatiotemporal dynamics of the long-range proximity effects that extend beyond the optical focal spot. Here, we empirically investigate these proximity effects in serial TPL using custom-made acrylate-based photoresists. We demonstrate that the complex spatiotemporal dynamics of the proximity effects can be explained through the kinetics of the reaction–diffusion photopolymerization mechanisms that underlie the curing process. Specifically, we show that the proximity effects arise due to comparable timescales of reaction and diffusion of oxygen. Furthermore, we demonstrate that long-range proximity effects can be suppressed by introducing phenolic inhibitors, which reduce oxygen consumption through non-inhibiting side reactions and promote faster termination of curing reactions. These insights enabled improving the linewidths from >400 to 260 nm during printing of nanoporous 3D woodpiles at scanning speeds of 50 mm s −1 . Thus, the knowledge generated here can be applied to deterministically tune the proximity effects and enable rapid printing of high-resolution nanoporous 3D structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Active space selection with self-healing diffusion Monte Carlo algorithms for periodic solids

Multideterminant Diffusion Monte Carlo (DMC) displays improved accuracy over single determinant DMC. Self-Healing Diffusion Monte Carlo (SHDMC) is a DMC based method that iteratively improves a multideterminant trial wavefunction. Although configuration interaction or complete active space (CAS) methods are very accurate and computationally feasible for many systems, they are not optimal for application to solids. SHDMC is accurate and designed for application to solids, so developing SHDMC based active space selection algorithms is a worthy endeavor. Here, we present and compare active space selection algorithms that are designed for use in conjunction with SHDMC, without relying on external approaches. For benchmarking, we calculated the ground state energy of a small unit cell of graphene and compared the results with a complete basis set extrapolated selected CI and a reference SHDMC trajectory. We found that systematically expanding the active space using an “auto-branching” algorithm optimally balances accuracy with computational practicality. To the best of our knowledge, this is the first work that demonstrates completely self-contained DMC-based active space selection algorithms that do not depend on external methods for determinant selection.

Spanedda, Nicole [ORNL]↗