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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Structures and diffusion of two-dimensional dusty plasmas on one-dimensional periodic substrates

Using numerical simulations, we examine the structure and diffusion of a two-dimensional dusty plasma (2DDP) in the presence of a one-dimensional periodic substrate (1DPS) as a function of increasing substrate strength. Both the pair correlation function perpendicular to the substrate modulation and the mean-squared displacement (MSD) of dust particles are calculated. It is found that both the structure and dynamics of 2DDP exhibit strong anisotropic effects, due to the applied 1DPS. As the substrate strength increases from zero, the structure order of dusty plasma along each potential well of 1DPS increases first probably due to the competition between the interparticle interactions and the particle-substrate interactions, and then decreases gradually, which may be due to the reduced dimensionality and the enhanced fluctuations. The obtained MSD along potential wells of 1DPS clearly shows three processes of diffusion in our studied 2DDP. Finally, between the initial ballistic and finally diffusive motion, there is the intermediate subdiffusion discovered here, which may result from the substrate-induced distortion of the caging dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Diffusion of self-propelled particles in complex media

The diffusion of active microscopic organisms in complex environments plays an important role in a wide range of biological phenomena from cell colony growth to single organism transport. Here, we investigate theoretically and computationally the diffusion of a self-propelled particle (the organism) embedded in a complex medium composed of a collection of nonmotile solid particles that mimic soil or other cells. Under such conditions we find that the rotational relaxation time of the swimming direction depends on the swimming velocity and is drastically reduced compared to a pure Newtonian fluid. This leads to a dramatic increase (of several orders of magnitude) in the effective rotational diffusion coefficient of the self-propelled particles, which can lead to “self-trapping” of the active particles in such complex media. An analytical model is put forward that quantitatively captures the computational results. Our work sheds light on the role that the environment plays in the behavior of active systems and can be generalized in a straightforward fashion to understand other synthetic and biological active systems in heterogenous environments.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Kinetic Theory of Spin Diffusion and Superdiffusion in X X Z Spin Chains

We address the nature of spin transport in the integrable XXZ spin chain, focusing on the isotropic Heisenberg limit. We calculate the diffusion constant using a kinetic picture based on generalized hydrodynamics combined with Gaussian fluctuations: we find that it diverges, and show that a self-consistent treatment of this divergence gives superdiffusion, with an effective time-dependent diffusion constant that scales as $D(t) \sim t^{1/3}$. This exponent had previously been observed in large-scale numerical simulations, but had not been theoretically explained. We briefly discuss XXZ models with easy-axis anisotropy $\Delta > 1$. Our method gives closed-form expressions for the diffusion constant $D$ in the infinite-temperature limit for all $\Delta > 1$. We find that $D$ saturates at large anisotropy, and diverges as the Heisenberg limit is approached, as $D \sim (\Delta - 1)^{-1/2}$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Studies of charm quark diffusion inside jets using PbPb and pp collisions at $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV

The first study of charm quark diffusion with respect to the jet axis in heavy ion collisions is presented. The measurement is performed using jets with $p^{jet}_T > 60 GeV / c$ and $D^0$ mesons with $p^D_T > 4 GeV / c$ in lead-lead (Pb-Pb) and proton-proton ($pp$) collisions at a nucleon-nucleon center-of-mass energy of $\sqrt{s_{NN}} = 5.02 TeV$, recorded by the CMS detector at the LHC. The radial distribution of $D^0$ mesons with respect to the jet axis is sensitive to the production mechanisms of the meson, as well as to the energy loss and diffusion processes undergone by its parent parton inside the strongly interacting medium produced in Pb-Pb collisions. When compared to Monte Carlo event generators, the radial distribution in $pp$ collisions is found to be well described by pythia, while the slope of the distribution predicted by sherpa is steeper than that of the data. In Pb-Pb collisions, compared to the $pp$ results, the $D^0$ meson distribution for $4 < p^D_T < 20 GeV / c$ hints at a larger distance on average with respect to the jet axis, reflecting a diffusion of charm quarks in the medium created in heavy ion collisions. At higher $p^D_T$, the Pb-Pb and $pp$ radial distributions are found to be similar.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Role of short- and long-range ordering on diffusion in Ni-Al alloys

The varying degrees of short- and long-range order exhibited by multicomponent solids complicates a first-principles calculation of nondilute diffusion coefficients. Temporal and spatial variations in the local degree of order affect the migration barriers of individual hops and can result in strong correlations between successive hops that ultimately affect macroscopic transport coefficients. Here we report on a first-principles study of diffusion in Ni-rich Ni-Al alloys. We used cluster expansion Hamiltonians to describe the energies of the end states and migration barriers of each hop. Kinetic Monte Carlo simulations were performed to calculate macroscopic transport coefficients. Variations in the degree of ordering are shown to play a significant role in affecting diffusion coefficients. While Al has a higher mobility than Ni in the disordered Ni-rich fcc solid solution, it becomes significantly less mobile in the ordered L1 2 γ' phase due to strong thermodynamic tendencies that keep Al trapped to disconnected sublattice sites.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2018

Internal Resistive Barriers Related to Zinc Diffusion During the Growth of Inverted Metamorphic Multijunction Solar Cells

Majority carrier barriers at heterointerfaces are a common source of non-linear resistance that hinders concentrator solar cell performance. The source of a particular barrier is often unclear in a multijunction device with numerous heterointerfaces. In this work, we demonstrate Zn-dopant diffusion during inverted metamorphic multijunction (IMM) device growth to be one key cause of internal barrier formation. Using an inverted GaAs/GaAs tandem solar cell with a high temperature annealing layer grown in between each subcell, we simulate the annealing conditions of a multijunction growth in a simplified structure. Through analysis of the device by secondary ion mass spectrometry (SIMS) and electrochemical capacitance-voltage profiling, we show that annealing causes Zn to diffuse out of the top cell Ga 0.5 In 0.5 P back surface field (BSF) and accumulate in the GaAs base. Through equilibrium band modeling, we show that the resultant doping profile forms an energetic barrier to hole flow in the valence band, which correlates with fill factor losses in the current-voltage curves measured under concentration. When we, instead, employ a C-doped Al 0.2 Ga 0.8 As BSF layer in the top cell, we do not observe evidence of a heterojunction barrier. We attribute this difference to the reduced diffusivity of carbon, confirmed by SIMS, as well as more favorable valence band offsets between GaAs and Al 0.2 Ga 0.8 As. Finally, we compare 5-junction IMM cells with Al 0.2 Ga 0.8 As:C and Ga 0.5 In 0.5 P:Zn BSF layers in the GaAs third junction, respectively, and show a significantly improved concentrator device performance when Al 0.2 Ga 0.8 As:C is employed. Lastly, we demonstrate the importance of designing annealing tolerance into multijunction structures that are subjected to extended annealing during growth.

14 SOLAR ENERGY↗
DOE OSTI2021

Dopant Diffusion Control for Improved Tandem Cells Grown by D-HVPE

GaInP top cell current-density presently limits the performance of HVPE-grown two-junction devices, in large part due to unwanted dopant diffusion. Here, we institute mitigation strategies to lower the diffusion of dopants from both the front contact and back surface field. Successful application of these strategies resulted in a short-circuit current density of 12.1 mA/cm 2 in a GaInP/GaAs cell, an improvement of 0.9 mA/cm2 over our previous best cell. The reduced Se diffusion results in a thinner unpassivated emitter, which can lead to higher series resistance. Despite the increased resistance we obtained an efficiency increase from 23.7% to 24.8%.

42 ENGINEERING↗
DOE OSTI2025

Importance Sampling Model-Based Diffusion for Trajectory Optimization

Trajectory optimization for robotic systems remains a challenging problem. This is especially true for robotic systems featuring nonlinear dynamics and many degrees of freedom. Data-based or model-free diffusion has recently been popularized in the fields of artificial intelligence and trajectory optimization. Model-Based Diffusion provides a data-free method of trajectory optimization, trained at runtime on a system dynamics model, suitable for high-dimensional models. This paper examines how importance sampling can enhance the performance of Model-Based Diffusion for trajectory optimization. Here, we quantify the benefits of importance sampling across three long horizon planning tasks. These results show as much as a 13x improvement in sample efficiency depending on environment and optimization parameters.

Golembeski, Seth [Georgia Institute of Technology,↗
DOE OSTI2022

Coupled diffusion-deformation-damage model for polymers used in hydrogen infrastructure

The soft materials used in the infrastructure of hydrogen storage and distribution systems are vulnerable because exposure to high-pressure hydrogen can lead to mechanical damage and property degradation. Some materials, such as polymers, can undergo mechanical failure due to a phenomenon wherein hydrogen gas diffuses through the polymer chains and occupies preexisting cavities or voids inside the polymer material. If the hydrogen gas pressure is reduced more quickly than hydrogen can diffuse back out of the polymer which is necessary for some applications, the trapped hydrogen gas instead escapes by rupturing the material, causing surface blistering or permanent damage. In this study, a continuum mechanics-based fully coupled diffusion-deformation model with damage is developed to predict the stress distribution and damage propagation while the polymer undergoes rapid decompression failure. The hyperelastic material model, along with the maximum principal strain failure theory, was chosen for this study as it represents the nonlinear material response with brittle failure observed in uniaxial tensile tests perfectly. EPDM polymer was chosen for this study because of its commercial availability and common use in hydrogen storage and distribution system. It has superior mechanical properties, high and low temperature resistance, and certain compounds work well in hydrogen gas. This work is useful for design engineers to alter the parameters while manufacturing polymer composites to increase their performance in a high-pressure hydrogen environment.

Kulkarni, Shank S.↗
DOE OSTI2024

Persistent enhancement of exciton diffusivity in CsPbBr 3 nanocrystal solids

In semiconductors, exciton or charge carrier diffusivity is typically described as an inherent material property. Here, we show that the transport of excitons among CsPbBr 3 perovskite nanocrystals (NCs) depends markedly on how recently those NCs were occupied by a previous exciton. Using transient photoluminescence microscopy, we observe a striking dependence of the apparent exciton diffusivity on excitation laser power that does not arise from nonlinear exciton-exciton interactions or thermal heating. We interpret our observations with a model in which excitons cause NCs to transition to a long-lived metastable configuration that markedly increases exciton transport. The exciton diffusivity observed here (>0.15 square centimeters per second) is considerably higher than that observed in other NC systems, revealing unusually strong excitonic coupling between NCs. The finding of a persistent enhancement in excitonic coupling may help explain other photophysical behaviors observed in CsPbBr 3 NCs, such as superfluorescence, and inform the design of optoelectronic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mapping structures and dynamics with frequency-correlated diffusion exchange

Understanding molecular motion in diffusion-driven complex environments is critical for designing sustainable materials and improving chemical processes. Here, we introduce a multidimensional nuclear magnetic resonance (NMR) method that captures how molecular populations exchange across different dynamic regimes. By extending the modulated gradient spin-echo technique to include frequency-frequency correlations, our approach reveals diffusion pathways that are otherwise obscured in heterogeneous systems. Implemented on a unilateral NMR magnet, the method eliminates gradient pulsing constraints and accesses dynamics in the kilohertz regime. We apply this technique to swelling and acid-catalyzed deconstruction of cross-linked and linear polymers to observe how structural heterogeneity evolves over time. By linking molecular motion to topology and chemical state, we extract physical metrics such as fractal surface dimensionality and reaction wavefront velocity, properties inaccessible with standard diffusion measurements. This work expands the capabilities of NMR for probing soft matter, with implications for polymer recycling and materials design.

Fricke, Sophia N. [University of California, Berke↗
DOE OSTI2018

Diffusion Blurring of GaAs Quantum Wells Grown at Low Temperature

The processes of the diffusion blurring of a periodic system of GaAs quantum wells separated by AlGaAs barriers are studied by photoluminescence spectroscopy. The system is grown by molecular-beam epitaxy at a low temperature (200°C) and additionally doped with Sb and P isovalent impurities. Postgrowth annealing at the temperature 750°C for 30 min induces an increase in the energy corresponding to the photoluminescence peak of the e1–hh1 exciton state in quantum wells because of blurring of the epitaxial GaAs/AlGaAs interfaces due to enhanced Al–Ga interdiffusion in the cation sublattice. For the Al concentration profile defined by linear diffusion into quantum wells, the Schrödinger equation for electrons and holes is solved. It is found that the experimentally observed energy position of the photoluminescence peak corresponds to the Al–Ga interdiffusion length 3.4 nm and to the effective diffusion coefficient 6.3 × 10{sup –17} cm{sup 2} s{sup –1} at the temperature 750°C. This value is found to be close to the corresponding value for GaAs quantum wells grown at low temperatures without additional doping with Sb and P impurities. From the results obtained in the study, it is possible to conclude that enhanced As–Sb and As–P interdiffusion in the anion sublattice only slightly influences the processes of Al–Ga interdiffusion in the cation sublattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

A Domain Decomposition Model Reduction Method for Linear Convection-Diffusion Equations with Random Coefficients

We developed a domain decomposition model reduction method for linear steady- state convection-diffusion equations with random coefficients. Of particular interest to this effort are the diffusion equation with random diffusivity and the convection-dominated transport equation with random velocity. We investigated two types of random fields, i.e., colored noises and discrete white noises, both of which can lead to high-dimensional parametric dependence in practice. The motivation is to exploit domain decomposition to reduce the parametric dimension of local problems in subdomains, such that an entire parametric map can be approximated with a small number of expensive partial differential equation (PDE) simulations. The new method combines domain decomposition with model reduction and sparse polynomial approximation, so as to simultaneously handle the high dimensionality and irregular behavior of the PDEs under consideration. Here, the advantages of our method lie in three aspects: (i) online-offline decomposition, i.e., the online cost is independent of the size of the triangle mesh; (ii) sparse approximation of operators involving nonaffine high-dimensional random fields; (iii) an effective strategy to capture irregular behaviors, e.g., sharp transitions of the PDE solutions. Two numerical examples are provided to demonstrate the advantageous performance of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Investigating Liquid Water Transport in Different Pore Structure of Gas Diffusion Layers for PEMFC Using Lattice Boltzmann Method

Proton exchange membrane fuel cells (PEMFC) require a gas diffusion layer (GDL) to aid in the transport of liquid fuel to the catalyst layer. In this work, direct modeling using the Lattice Boltzmann Method (LBM) was applied to X-ray CT scans of four different carbon gas diffusion layers to understand the mass transport properties through the samples. Three injection orientations were used to study local saturation levels, water evolution through the sample, and mass transport behavior at breakthrough conditions. The LBM, combined with computational fluid dynamic modeling techniques, can accurately predict liquid saturation at the macro and micro scale, which provides more insight into the mass transport phenomena through the GDL. The change of pore structure and orientation in both the in-plane and through-plane determines the path that liquid water must take, which could aid or impact PEMFC performance. The outcomes from this work will also benefit any research that needs knowledge of internal mass transport qualities of gas diffusion media.

Sepe, M.↗
DOE OSTI2020

Bridging Scales to Model Reactive Diffusive Transport in Porous Media

Published by ECS. Two novel scale-bridging algorithms to model reaction-diffusion transport in porous media are presented. The algorithms are based on direct numerical simulations and couple the information of a micro-scale model, which accounts for the large field of view provided by micro X-ray computed tomography (X-ray CT), and a nano-scale model, which locally resolves transport in the fine structure extracted from nano X-ray CT. The micro-scale model is discretized in the through-plane direction into a 1D grid, where effective properties and internal boundaries are determined based on the results from the nano-scale model. The validated algorithms are used to examine transport of oxygen in precious group metal-free electrodes considering both zero- and first-order kinetics. Unlike conventional methods, the results show that the effective diffusivity is not a passive property but increases in regions where the reaction-rate coefficient is large. The proposed algorithms account for the multiscale coupling of reaction-diffusion transport and material microstructure, thus improving the predictions compared to conventional methods.

42 ENGINEERING↗
DOE OSTI2019

Secondary Pore Network Design Optimization Analytical Diffusion Model for Lithium Ion Battery

Fast ion transport along the electrode thickness is required for Lithium ion battery (LIB) to sustain continuous high-rate discharge or charge currents, which is a desired feature for the fast charging of electric vehicle batteries. Advance in LIB electrode manufacturing allows to control, to a certain extent, the electrode microstructure architecture in order to improve the macroscopic performances of these devices. For instance, several groups have experimentally tested architectures tailored specifically to increase the through plane ionic diffusion in regards for fast charge application by adding channels or groove-like large pores oriented along the electrode thickness. These voids do not replace the existing pores of the electrode microstructure but consist in a second, coarser, pore network, called the secondary pore network (SPN). The model quantifies the impact of SPN on ionic diffusion with a composite electrode through a simple analytical approach, considering various pore channel geometries and comparing to standard electrodes with identical gravimetric and identical volumetric specific theoretical capacity. The goal is to find optimal SPN design parameters according to three optimization objectives aiming to balance the improved overall through-plane, thanks to the coarse aligned channels, and the degraded in-plane diffusion, due to the porous matrix densification required to maintain gravimetric and volumetric specific theoretical capacity. The analytical model must be considered as a pre-screening tool to quickly investigate large design space, that can be refined later with more complex electrochemical CPU-expensive numerical models.

Usseglio Viretta, Francois↗
DOE OSTI2023

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.

Kim, Hyojin↗