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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Phenethylammonium Functionalization Enhances Near-Surface Carrier Diffusion in Hybrid Perovskites

Understanding semiconductor surface properties and manipulating them chemically are critical for improving their performance in optoelectronic devices. Hybrid halide perovskites have emerged as an exciting class of highly efficient solar materials; however, their device performance could be limited by undesirable surface properties that impede carrier transport and induce recombination. Here we show that surface functionalization of methylammonium lead iodide (MAPbI 3 ) perovskite with phenethylammonium iodide (PEAI), a commonly employed spacer cation in two-dimensional perovskites, can enhance carrier diffusion in the near-surface regions and reduce defect density by more than one order of magnitude. Using transient transmission and reflection microscopy, we selectively imaged the transport of the carriers near the (001) surface and the bulk carriers in single-crystal MAPbI 3 microplates. The surface functionalization increases the diffusion coefficient of the carriers in a 40-nm subsurface region from ~ 0.6 cm 2 s -1 to ~ 1 cm 2 s -1 , similar to the value for bulk carriers. These results suggest the PEA ligands are effective in suppressing surface defect and phonon scattering and shed light on the mechanisms for enhancing photophysical properties and improving solar cell efficiency.

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

Reconstruction of Electron and Ion Distribution Functions in a Magnetotail Reconnection Diffusion Region

In the diffusion region of magnetotail reconnection, particle distributions are highly structured, exhibiting triangular shapes and multiple striations that deviate dramatically from the Maxwellian distribution. Fully kinetic simulations have been demonstrated to be capable of producing the essential structures of the observed distribution functions, yet are computationally not feasible for 3D global simulations. The fluid models used for large-scale simulations, on the other hand, do not have the kinetic physics necessary for describing reconnection accurately. Our study aims to bridge fully kinetic and fluid simulations by quantifying the information required to capture the non-Maxwellian features in the distributions underlying the closures used in the fluid code. Here, we compare the results of fully kinetic simulations with observed electron velocity distributions in a magnetotail reconnection diffusion region and use the maximum entropy model to reconstruct electron and ion distributions using various numbers of moments obtained from the simulation. Our results indicate that using only local moments, the maximum entropy model can reproduce many of the features of the distributions: (1) the electron outflow distribution with a tilted triangular structure is reproduced with 21 or more moments in agreement with Ng et al. and (2) counterstreaming distributions can be captured with the 35-moment model when the separation in velocity space between the populations is large.

58 GEOSCIENCES↗
DOE OSTI2021

Estimating and Forecasting Time-Varying Groundwater Recharge in Fractured Rock: A State-Space Formulation with Preferential and Diffuse Flow to the Water Table

Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.

groundwater, Recharge, infiltration, recursive est↗
DOE OSTI2022

Lower‐Hybrid Wave Structures and Interactions With Electrons Observed in Magnetotail Reconnection Diffusion Regions

Abstract We investigate waves close to the lower‐hybrid frequency in 12 magnetotail reconnection electron diffusion region (EDR) events with guide field levels of near‐zero to 30%. In about half of the events, the wave vector has a small component along the current sheet normal, consistent with known lower‐hybrid drift wave properties, but the perpendicular magnetic field fluctuations can be comparable or greater than the parallel component, a feature unique to the waves inside and adjacent to EDRs. Another new wave property is that the wave vector has a significant component along the current sheet normal in some events and completely along the normal for one event. In 1/4 of the events, the term has a significant contribution to the wave electric field, possibly a feature of lower‐hybrid waves more likely to exist in the diffusion region than further away from the X‐line. Electron temperature variations are correlated with the wave potential, due to wave electric field acceleration and crossings at the corrugated separatrix region with different amounts of mixing between reconnection inflowing and outflowing populations. The latter also leads to the anti‐correlation between parallel and perpendicular temperature components. Using four‐spacecraft measurements, the magnetic field line twisting is demonstrated by the correlated fluctuations in and . The lower‐hybrid wave in the EDR of weak guide field reconnection may be generated near separatrices and penetrate to the mid‐plane or locally generated, and the latter possibility is beyond the prediction of previous reconnection simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

2D Reconstruction of Magnetotail Electron Diffusion Region Measured by MMS

Models for collisionless magnetic reconnection in near-Earth space are distinctly characterized as 2D or 3D. In 2D kinetic models, the frozen-in law for the electron fluid is usually broken by laminar dynamics involving structures set by the electron orbit size, while in 3D models the width of the electron diffusion region is broadened by turbulent effects. We present an analysis of in situ spacecraft observations from the Earth's magnetotail of a fortuitous encounter with an active reconnection region, mapping the observations onto a 2D spatial domain. While the event likely was perturbed by low-frequency 3D dynamics, the structure of the electron diffusion region remains consistent with results from a 2D kinetic simulation. As such, the event represents a unique validation of 2D kinetic, and laminar reconnection models.

79 ASTRONOMY AND ASTROPHYSICS↗
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 OSTI2021

Enhancing carbon dioxide gas-diffusion electrolysis by creating a hydrophobic catalyst microenvironment

Electroreduction of carbon dioxide (CO 2 ) over copper-based catalysts provides an attractive approach for sustainable fuel production. While efforts are focused on developing catalytic materials, it is also critical to understand and control the microenvironment around catalytic sites, which can mediate the transport of reaction species and influence reaction pathways. Here, we show that a hydrophobic microenvironment can significantly enhance CO 2 gas-diffusion electrolysis. For proof-of-concept, we use commercial copper nanoparticles and disperse hydrophobic polytetrafluoroethylene (PTFE) nanoparticles inside the catalyst layer. Consequently, the PTFE-added electrode achieves a greatly improved activity and Faradaic efficiency for CO 2 reduction, with a partial current density >250 mA cm -2 and a single-pass conversion of 14% at moderate potentials, which are around twice that of a regular electrode without added PTFE. The improvement is attributed to a balanced gas/liquid microenvironment that reduces the diffusion layer thickness, accelerates CO 2 mass transport, and increases CO 2 local concentration for the electrolysis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Pseudo-bilayer architecture enables high-performance organic solar cells with enhanced exciton diffusion length

Solution-processed organic solar cells (OSCs) are a promising candidate for next-generation photovoltaic technologies. However, the short exciton diffusion length of the bulk heterojunction active layer in OSCs strongly hampers the full potential to be realized in these bulk heterojunction OSCs. Herein, we report high-performance OSCs with a pseudo-bilayer architecture, which possesses longer exciton diffusion length benefited from higher film crystallinity. This feature ensures the synergistic advantages of efficient exciton dissociation and charge transport in OSCs with pseudo-bilayer architecture, enabling a higher power conversion efficiency (17.42%) to be achieved compared to those with bulk heterojunction architecture (16.44%) due to higher short-circuit current density and fill factor. A certified efficiency of 16.31% is also achieved for the ternary OSC with a pseudo-bilayer active layer. Our results demonstrate the excellent potential for pseudo-bilayer architecture to be used for future OSC applications.

14 SOLAR ENERGY↗
DOE OSTI2023

Probing the atomically diffuse interfaces in Pd@Pt core-shell nanoparticles in three dimensions

Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is found to be atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle’s morphology or crystallographic texture. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds, which is confirmed by atomic images of Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Charge decay in the spatial afterglow of plasmas and its impact on diffusion regimes

Abstract The spatial afterglow is a region at the boundary of a non-equilibrium plasma where charged species relax into ambient equilibrium. In many applications, the spatial afterglow is the part of the plasma that interacts with surfaces, such as suspended particles or a material substrate. However, compared to the bulk plasma, there has been little effort devoted to studying the properties of the spatial afterglow, and a fundamental analysis has not yet been developed. Here, we apply double Langmuir probe measurements and develop an advection-diffusion-recombination model to provide a detailed description of charged species in the spatial afterglow over a wide range of pressures, temperatures, plasma dimensions, and flow rates. We find that the density of charged species in the spatial afterglow decays by orders of magnitude, which leads to a transition from ambipolar to free diffusion. These insights can be used to explain or predict experimental observations of phenomena, such as the charging of dust grains and the dose of charged species to a biomaterial.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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 OSTI2021

Atomistic observation on diffusion-mediated friction between single-asperity contacts

Super-lubricity, a sliding regime in which friction almost vanishes, has been well-documented with the advent of atomic force microscopy (AFM) and molecular dynamics (MD) simulation due to its enormous implications for manufacturing micro-/nano-electromechanical systems (MEMS/NEMS). Nevertheless, the atomistic mechanisms for super-lubricious behaviors are still elusive, primarily due to a lack of direct observation of interface at atomic resolution during frictional process. Here by using in situ high-resolution transmission electron microscopy (HRTEM) coupled with AFM, we report diffusion-mediated formation of a loosely-packed interfacial layer between two metallic asperities at infinitesimal normal forces, which is responsible for the sliding regime, super-lubricity exhibiting ultra-low friction forces and continuous sliding. By contrast, the loosely-packed interfacial layer vanishes along with the super-lubricity, leading to typical stick-slip friction. Additionally, in situ TEM observation and MD simulations reveals the crucial role that atom diffusion plays in atomic friction, and provides new insights into the fundamental mechanisms of super-lubricity.

42 ENGINEERING↗
DOE OSTI2022

Nonlinear model of infection wavy oscillation of COVID-19 in Japan based on diffusion kinetics

The infectious propagation of SARS-CoV-2 is continuing worldwide, and specifically, Japan is facing severe circumstances. Medical resource maintenance and action limitations remain the central measures. An analysis of long-term follow-up reports in Japan shows that the infection number follows a unique wavy oscillation, increasing and decreasing over time. However, only a few studies explain the infection wavy oscillation. This study introduces a novel nonlinear mathematical model of the new infection wavy oscillation by applying the macromolecule diffusion theory. In this model, the diffusion coefficient that depends on population density gives nonlinearity in infection propagation. As a result, our model accurately simulated infection wavy oscillations, and the infection wavy oscillation frequency and amplitude were closely linked with the recovery rate of infected individuals. In conclusion, our model provides a novel nonlinear contact infection analysis framework.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Emerging investigator series: ion diffusivities in nanoconfined interfacial water films contribute to mineral carbonation thresholds

The dynamics and reactivity of nanoconfined fluids play critical roles across a wide range of environmental and technological systems, though reaction mechanisms and kinetics are not well understood. Here, the carbonation kinetics of forsterite (Mg 2 SiO 4 ) exposed to 90 atm supercritical carbon dioxide at 35–65 °C and 85–100% relative humidity (RH) was monitored with in situ X-ray diffraction, and partner molecular dynamics simulations were used to describe the free energy landscape of Mg 2+ adsorption and diffusion on forsterite surfaces covered in water films 3–10 monolayers thick. The collective findings reveal how decreasing the water film thickness by ~1.4 monolayers, from ~0.92 to ~0.64 nm, inhibited reaction rates by up to 97%, promoted anhydrous Mg-carbonate (magnesite, MgCO 3 ) precipitation, and more than doubled the apparent activation energy of carbonation. The transport simulations suggest that four monolayers are required to enable sufficiently facile Mg 2+ diffusion, helping explain previously observed water film thickness-dependent reactivity thresholds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Molecular interactions in diffusion-controlled aldol condensation with mesoporous silica nanoparticles

The aldol reaction of p-nitrobenzaldehyde in amino-catalyzed mesoporous silica nanoparticles (MSN) has revealed varying catalytic activity with the size of the pores of MSN. The pore size dependence related to the reactivity indicates that the diffusion process is important. A detailed molecular-level analysis for understanding diffusion requires assessment of the noncovalent interactions of the molecular species involved in the aldol reaction with each other, with the solvent, and with key functional groups on the pore surface. Such an analysis is presented here based upon the effective fragment potential (EFP). The EFP method can calculate the intermolecular interactions, decomposed into Coulomb, polarization, dispersion, exchange-repulsion, and charge-transfer interactions. In this study, the potential energy surfaces corresponding to each intermolecular interaction are analyzed for homo- and hetero-dimers with various configurations. The monomers that compose dimers are five molecules such as p-nitrobenzaldehyde, acetone, n-hexane, propylamine, and silanol. The results illustrate that the dispersion interaction is crucial in most dimers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350–500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. Here, we also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗