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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

Development of APCVD BSG and POCl 3 Codiffusion Process for Double-Side TOPCon Solar Cell Precursor Fabrication

This paper presents a commercially viable process for fabricating a high-quality double-side tunnel oxide passivating contact (DS-TOPCon) cell precursor using APCVD-deposited boron silicate glass and ex-situ POCl 3 diffusion in a single high-temperature step, eliminating the need for additional masking and diffusion processes. A two-tier temperature profile was developed, involving a pre-annealing at above 900°C in nitrogen (N2) ambient followed by POCl 3 diffusion at 840°C. We investigated the effect of varying pre-annealing temperatures, ranging from 875°C to 950°C, on the passivation quality and metal-Si contact properties of both n-TOPCon and p-TOPCon layers. The resultant DS-TOPCon cell precursor after silicon nitride (SiNX) passivation exhibited an excellent iV OC of close to 730 mV. In addition, a rapid asymmetric poly-Si thinning technique, developed in this work, enabled adjustment of the front n + poly-Si thickness while maintaining the rear p + poly-Si thickness. Two types of DS-TOPCon cell architectures can be fabricated: i) full-area thin (≈40nm) n-TOPCon layer on the front and ii) selective-area thick (≈200nm) n-TOPCon fingers underneath the metal grid. Device simulations suggest that full-area DS-TOPCon cell with 40 nm n + poly-Si and selective-area DS-TOPCon cell with 200 nm n + poly fingers on the front, fabricated from our current DS-TOPCon cell precursor, can achieve cell efficiencies of 22.1 and 23.5%, respectively. Detailed power loss analysis and device simulation reveal that further improvements in material and device parameters have potential to push the cell efficiencies of DS-TOPCon cell structure beyond 25%, making it a promising alternative to fabricate a high-efficiency next-generation solar cells at low cost.

14 SOLAR ENERGY↗
DOE OSTI2019

Quantum Effects on H 2 Diffusion in Zeolite RHO: Inverse Kinetic Isotope Effect for Sieving

We use canonical variational theory (CVT) with small-curvature tunneling (SCT) contributions to investigate quantum effects on the H 2 diffusion process in the pure-silica zeolite RHO. At low temperature we find an inverse kinetic isotopic sieving effect in that the heavier isotopic species diffuses faster than the lighter one. Three quantum effects contribute to this kinetic isotope effect (KIE). The first one is quantum mechanical tunneling; this—on its own—would lead to a normal kinetic isotopic sieving effect, in which lighter diprotium diffuses faster than dideuterium. The second factor, which we find to dominate in the present case, is zero-point energy (ZPE). Deuterium has a lower ZPE, which leads to a smaller effective barrier for tunneling because the transition state has a larger ZPE than the precursor stable state; this results in an inverse KIE. The third factor, the thermal vibrational energy (computed from the quantized vibrational partition function), also favors a normal KIE, but it is outweighed by the ZPE effect. The vibrations of the zeolite host framework are found to play an important role at low temperatures, and our calculations consider up to 7296 degrees of freedom at the equilibrium structure and the saddle point and up to 221 degrees of freedom along the reaction path. In conclusion, the importance of quantum considerations on the dynamics is elucidated by comparison to a purely classical treatment.

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

Pattern formation in a coupled membrane-bulk reaction-diffusion model for intracellular polarization and oscillations

Reaction-diffusion systems have been widely used to study spatio-temporal phenomena in cell biology, such as cell polarization. Coupled bulk-surface models naturally include compartmentalization of cytosolic and membrane-bound polarity molecules. Here we study the distribution of the polarity protein Cdc42 in a mass-conserved membrane-bulk model, and explore the effects of diffusion and spatial dimensionality on spatio-temporal pattern formation. We first analyze a one-dimensional (1-D) model for Cdc42 oscillations in fission yeast, consisting of two diffusion equations in the bulk domain coupled to nonlinear ODEs for binding kinetics at each end of the cell. In 1-D, our analysis reveals the existence of symmetric and asymmetric steady states, as well as anti-phase relaxation oscillations typical of slow-fast systems. We then extend our analysis to a two-dimensional (2-D) model with circular bulk geometry, for which species can either diffuse inside the cell or become bound to the membrane and undergo a nonlinear reaction-diffusion process. We also consider a nonlocal system of PDEs approximating the dynamics of the 2-D membrane-bulk model in the limit of fast bulk diffusion. In all three model variants we find that mass conservation selects perturbations of spatial modes that simply redistribute mass. In 1-D, only anti-phase oscillations between the two ends of the cell can occur, and in-phase oscillations are excluded. In higher dimensions, no radially symmetric oscillations are observed. Instead, the only instabilities are symmetry-breaking, either corresponding to stationary Turing instabilities, leading to the formation of stationary patterns, or to oscillatory Turing instabilities, leading to traveling and standing waves. Codimension-two Bogdanov—Takens bifurcations occur when the two distinct instabilities coincide, causing traveling waves to slow down and to eventually become stationary patterns. Our work clarifies the effect of geometry and dimensionality on behaviors observed in ma.ss-conserved cell polarity models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Temperature Dependence of Self-diffusion in Cr2O3 from First Principles

Understanding and predicting the dominant diffusion processes in Cr2O3 is essen- tial to its optimization for anti-corrosion coatings, spintronics, and other applications. Despite significant theoretical effort in modeling defect mediated diffusion in Cr2O3 the correlation with experimentally measured diffusivities remains poor partly due to the insufficient accuracy of the theoretical approaches. Here an attempt to resolve these discrepancies is made through high accuracy density functional theory simulations cou- pled with grand canonical formalism of defect thermochemistry. In this approach, point defect formation energies were computed using hybrid exchange correlation functional. This level of theory proved to be essential for achieving the agreement with experi- mental self-diffusion coefficients. The analysis of the resulting self-diffusion coefficients indicate that chromium has higher mobility at low temperatures and high oxygen partial pressures, in particular at standard temperature and pressure conditions. At high vacuum, high temperature conditions, oxygen diffusion becomes dominant. At Cr/Cr2O3 interfaces O vacancies were found to be more mobile than Cr vacancies at all temperatures. Cr diffuses preferentially along the c-axis at low temperatures but switches to basal plane at higher temperatures. O diffusion is primarily bound to basal plane at all temperatures.

Medasani, Bharat K.↗
DOE OSTI2019

Effects of Moisture on Diffusion in Unmodified Wood Cell Walls: A Phenomenological Polymer Science Approach

Despite the importance of cell wall diffusion to nearly all aspects of wood utilization, diffusion mechanisms and the detailed effects of moisture remain poorly understood. In this perspective, we introduce and employ approaches established in polymer science to develop a phenomenological framework for understanding the effects of moisture on diffusion in unmodified wood cell walls. The premise for applying this polymer-science-based approach to wood is that wood polymers (cellulose, hemicelluloses, and lignin) behave like typical solid polymers. Therefore, the movement of chemicals through wood cell walls is a diffusion process through a solid polymer, which is in contrast to previous assertions that transport of some chemicals occurs via aqueous pathways in the cell wall layers. Diffusion in polymers depends on the interrelations between free volume in the polymer matrix, molecular motions of the polymer, diffusant dimensions, and solubility of the diffusant in the polymer matrix. Because diffusion strongly depends on whether a polymer is in a rigid glassy state or soft rubbery state, it is important to understand glass transitions in the amorphous wood polymers. Through a review and analysis of available literature, we conclude that in wood both lignin and the amorphous polysaccharides very likely have glass transitions. After developing and presenting this polymer-science-based perspective of diffusion through unmodified wood cell walls, suggested directions for future research are discussed. A key consideration is that a large difference between diffusion through wood polymers and typical polymers is the high swelling pressures that can develop in unmodified wood cell walls. This pressure likely arises from the hierarchical structure of wood and should be taken into consideration in the development of predictive models for diffusion in unmodified wood cell walls.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2021

A New Route to Quantum-Scale Structures through a Novel Enhanced Germanium Diffusion Mechanism

This project sought to develop a fundamental understanding of the mechanisms underlying a newly observed enhanced germanium (Ge) diffusion process in silicon germanium (SiGe) semiconductor nanostructures during thermal oxidation. Using a combination of oxidationdiffusion experiments, high resolution imaging, and theoretical modeling, a model for the enhanced Ge diffusion mechanism was proposed. Additionally, a nanofabrication approach utilizing this enhanced Ge diffusion mechanism was shown to be applicable to arbitrary 3D shapes, leading to the fabrication of stacked silicon quantum dots embedded in SiGe nanopillars. A new wet etch-based method for preparing 3D nanostructures for highresolution imaging free of obscuring material or damage was also developed. These results enable a new method for the controlled and scalable fabrication of on-chip silicon nanostructures with sub-10 nm dimensions needed for next generation microelectronics, including low energy electronics, quantum computing, sensors, and integrated photonics.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Analytical estimates of proton acceleration in laser-produced turbulent plasmas

With the advent of high power lasers, new opportunities have opened up for simulating astrophysical processes in the laboratory. We show that second-order Fermi acceleration can be directly investigated at the National Ignition Facility, Livermore. This requires measuring the momentum-space diffusion of 3 MeV protons produced within a turbulent plasma generated by a laser. In conclusion, treating Fermi acceleration as a biased diffusion process, we show analytically that a measurable broadening of the initial proton distribution is then expected for particles exiting the plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Materials path toward improved energy efficiency for High Energy Physics accelerators

For nearly fifty years researchers working with particle accelerators have envisioned reducing their initial and operating costs by substituting the intermetallic compound Nb 3 Sn for Nb as the cavity interior surface. The goal has remained elusive, stymied especially by the fall of cavity quality factor (representing increasing energy consumption) with increasing gradient (the amount of accelerator need to reach an energy goal). While one research group reported making a few cavities free of the problem, no one has reported how this comes about or has reproduced the improvement. We believe that insufficient understanding is responsible. We report here findings of a detailed, systematic study of the formation of Nb 3 Sn coatings on Nb, relating structure and properties of the coating to the process parameters and substrate materials on the one hand, and RF surface impedance and SRF cavity performance on the other was carried out, focusing on the two step (nucleation – deposition) tin vapor diffusion process. Major outcomes are: The choice of initial surface preparation (BCP etc) has no discernable impact on any quality of the final coating; Though tin chloride serves as the coating nucleant, no trace could be found by any means of chlorine after nucleation or coating; No evidence was found for any substantive impact of the details of the nucleation on the ultimate coating, as long as it was done within the frequently used range; The coating forms as Nb 3 Sn crystals by tin from the vapor diffusing down grain boundaries to the Nb interface and growing there. Higher temperature and longer time lead to larger, but otherwise substantially identical crystals; All coatings exhibited roughness on the micron scale. Testing the effect of coating surface modification technologies in use by the community, only a carefully tailored electropolish showed significant benefit; and, Preliminary results indicate that Ti contamination may be associated with falling gradient. Two particular item appear promising for future work. The effect of surface topography in the range seen here on performance is significant for niobium. The nature and impact on Nb 3 Sn, if any, needs to be understood and managed. At what level Ti, Ta or other impurities affects the performance of Nb 3 Sn needs to be understood.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solute Redistribution During Aging in a Laser-Based Powder Bed Fusion-Produced Fe-18Ni-9Co-5Mo-Ti Maraging Steel

Solute distribution during the laser-based powder bed fusion of metals process is highly complex because of solidification. This study builds on a previous investigation of how solute heterogeneities across different scales influence phase distribution and aging physical phenomena in a Fe-18Ni-9Co-5Mo-Ti maraging steel (Santana et al. in Addit Manuf 104494, 2024). Previous work using energy-dispersive X-ray spectroscopy revealed heterogeneous Ni and Mo enrichment at intercellular regions. Using X-ray diffraction, 8 ± 3% retained austenite was quantified in as-built samples. Additionally, electron backscatter diffraction analysis exposed the location of retained austenite along cell boundaries. In this work, scanning transmission electron microscopy results revealed the presence of non-enriched retained austenite in the as-built condition. With increased aging temperature, Ni and Mo enrichment in austenite increased, especially at the austenite/martensite boundary. Solute diffusion was simulated in DICTRA Thermo-Calc® software to assess solute redistribution between the matrix and second phases and to comprehend whether elemental diffusion processes are interconnected. Three different scenarios were considered: (1) solute diffusion in the matrix; (2) diffusion with enriched austenite in the intercellular region; (3) diffusion with non-enriched austenite in the intercellular region. Results showed that Ni partitioning controls austenite growth and that boundary regions exhibit higher element enrichment than the core.

42 ENGINEERING↗
DOE OSTI2017

Polarized IR and Raman spectra of (H{sub 2}O){sub n} clusters (n = 2–5) in the c-channels of apatite single crystals

The infrared (IR) and Raman spectra of three samples of fluorapatite single crystals and carbonate fluorapatite single crystal in the region of 7000–400 cm{sup –1} were studied. It has been shown that isomorphic substitution of the Ca{sup 2+} cation and the PO{sub 4}{sup 3-} anion leads to an increase in the size of the c-channels in the apatite crystal, which is accompanied by the formation of water clusters of the (H{sub 2}O){sub n} and H{sub 3}O{sup +} · (H{sub 2}O){sub n} (n = 2–5) types as a result of the diffusion processes in these local sections of the c-channels.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Effects of MnS Inclusions on the Banded Microstructure in Non-quenched and Tempered Steel

The effects of MnS inclusions in a non-quenched and tempered steel on banded ferrite/pearlite (F/P) microstructure have been studied in this investigation. The results show that the banded microstructure is mainly caused by fluctuations in the concentration of alloying elements such as Mn and Cr, which were determined by energy dispersive spectroscopy. As a manganese-rich precipitate, MnS inclusions might produce extra effects on the banded microstructure and diffusion behavior of alloying elements depending on their size and distribution. Large-sized MnS inclusions distributed within pearlite bands retard the diffusion process of alloying elements, and accordingly, aggregate the degree of banding. On the contrary, small-sized MnS inclusions are much smaller than average grain size, which have little effects on the diffusion of alloying elements on a large scale, and they can promote the nucleation of ferrite and lead to a uniform distribution of ferrite and pearlite. An extended homogenization treatment at high temperatures can convert large-sized MnS inclusions into evenly dispersed smaller inclusions, and accordingly reduce the effect of MnS inclusions on the final microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A comprehensive computational study of adatom diffusion on the aluminum (1 0 0) surface

The complexity of adatom diffusion on the Al (1 0 0) surface is reflected by the existence of several low-energy non-trivial atomic exchange or vacancy formation mechanisms involving concerted motion of several (3–7) atoms. Interestingly, these mechanisms have energy barriers lower than or comparable to that of the simple (and intuitive) hopping mechanism commonly observed on other surface facets. While prior studies mainly used classical potentials to understand diffusion processes active on Al (1 0 0) surface, we use accurate (and expensive) density functional theory (DFT) computations to estimate barriers associated with nine low-energy and non-trivial adatom diffusion mechanisms. We find that there exist several exchange mechanisms with energy barriers less than or equal to that of the trivial hop mechanism. Furthermore, several of the atomic exchange mechanisms have barriers within 0.2 eV of that of the simple hop, thereby highlighting mechanisms that can be relevant during surface/crystal growth. Our results paint a highly complex picture of the diffusion landscape on Al (1 0 0) and provide insights into how such mechanisms may contribute toward large length- and time-scale surface phenomena. The results presented in this work may also have implications for other fcc metals. Further, we show that some of the commonly used interatomic potentials fail to accurately capture the details of adatom diffusion on Al (1 0 0). The presented benchmark DFT dataset can thus be utilized to parameterize/retrain such potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Connecting the failure of K-theory inside and above vegetation canopies and ejection-sweep cycles by a large eddy simulation

Parameterizations of biosphere-atmosphere interaction processes in climate models and other hydrological applications require characterization of turbulent transport of momentum and scalars between vegetation canopies and the atmosphere, which is often modeled using a turbulent analogy to molecular diffusion processes. However, simple flux-gradient approaches (K-theory) fail for canopy turbulence. One cause is turbulent transport by large coherent eddies at the canopy scale, which can be linked to sweep-ejection events, and bear signatures of non-local organized eddy motions. K-theory, that parameterizes the turbulent flux or stress proportional to the local concentration or velocity gradient, fails to account for these non-local organized motions. The connection to sweep-ejection cycles and the local turbulent flux can be traced back to the turbulence triple moment $\overline{C'W'W'}$. In this work, we use large-eddy simulation to investigate the diagnostic connection between the failure of K-theory and sweep-ejection motions. Analyzed schemes are quadrant analysis (QA) and a complete and incomplete cumulant expansion (CEM and ICEM) method. The latter approaches introduce a turbulence timescale in the modeling. Furthermore, we find that the momentum flux needs a different formulation for the turbulence timescale than the sensible heat flux. In conclusion, accounting for buoyancy in stratified conditions is also deemed to be important in addition to accounting for non-local events to predict the correct momentum or scalar fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Tailorable Exciton Transport in Doped Peptide–Amphiphile Assemblies

Light-harvesting biomaterials are an attractive target in photovoltaics, photocatalysis, and artificial photosynthesis. Through peptide self-assembly, complex nanostructures can be engineered to study the role of chromophore organization during light absorption and energy transport. To this end, we demonstrate the one-dimensional transport of excitons along naturally occurring, light-harvesting, Zn-protoporphyrin IX chromophores within self-assembled peptide-amphiphile nanofibers. The internal structure of the nanofibers induces packing of the porphyrins into linear chains. We find that this peptide assembly can enable long-range exciton diffusion, yet it also induces the formation of excimers between adjacent molecules, which serve as exciton traps. Electronic coupling between neighboring porphyrin molecules is confirmed by various spectroscopic methods. The exciton diffusion process is then probed through transient photoluminescence and absorption measurements and fit to a model for one-dimensional hopping. Because excimer formation impedes exciton hopping, increasing the interchromophore spacing allows for improved diffusivity, which we control through porphyrin doping levels. We show that diffusion lengths of over 60 nm are possible at low porphyrin doping, representing an order of magnitude improvement over the highest doping fractions.

diffusion↗
DOE OSTI2023

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Leveraging inter-firm influence in the diffusion of energy efficiency technologies: An agent-based model

Energy efficiency technologies (EETs) are crucial for saving energy and reducing carbon dioxide emissions. However, the diffusion of EETs in small and medium-sized enterprises is rather slow. Literature shows the interactions between innovation adopters and potential adopters have significant impacts on innovation diffusion. Enterprises lack the motivation to share information, and EETs usually lack observability, which suppress the inter-firm influence. Thus, an information platform, together with proper policies encouraging or forcing enterprises to disclose EET-related information, should help harness inter-firm influence to accelerate EETs' diffusion. To explore whether and how such an information platform affects EETs' diffusion in small and medium-sized enterprises, this report builds an agent-based model to mimic EET diffusion processes. Based on a series of controlled numerical experiments, some counter-intuitive phenomena are discovered and explained. The results show that the information platform is a double-edged sword that notably accelerates EETs' diffusion by approximately 47% but may also boost negative information to diffuse even faster and delay massive adoption of EETs. Increasing network density and the intensity of inter-firm influence are effective to speed EET diffusion, but their impacts diminish drastically after reaching some critical values (0.05 and 0.15 respectively) and eventually harm the stability of the system. Ultimately, the findings implicate that EET suppliers should carefully launch their promising but immature products; policies that can reduce the perceived risk by enterprises and the effort to maintain an informative rather than judgmental information platform can prominently mitigate the negative side effects brought by high fluidity of information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

GP-BayesOpInf

SAND2025-01851O GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗