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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Origin of the 2D Electron Gas at the SrTiO 3 Surface

Bulk SrTiO 3 is a well-known band insulator and the most common substrate used in the field of complex oxide heterostructures. Its surface and interface with other oxides, however, have demonstrated a variety of remarkable behaviors distinct from those expected. In this work, using a suite of in situ techniques to monitor both the atomic and electronic structures of the SrTiO 3 (001) surface prior to and during growth, the disappearance and re-appearance of a 2D electron gas (2DEG) is observed after the completion of each SrO and TiO 2 monolayer, respectively. The 2DEG is identified with the TiO 2 double layer present at the initial SrTiO 3 surface, which gives rise to a surface potential and mobile electrons due to vacancies within the TiO 2-x adlayer. Much like the electronic reconstruction discovered in other systems, two atomic planes are required, here supplied by the double layer. The combined in situ scattering/spectroscopy findings resolve a number of longstanding issues associated with complex oxide interfaces, facilitating the employment of atomic-scale defect engineering in oxide electronics.

2D electron gas↗
DOE OSTI2021

CLMM : a LSST-DESC cluster weak lensing mass modeling library for cosmology

ABSTRACT We present the v1.0 release of CLMM, an open source python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a stand-alone toolkit of building blocks to enable end-to-end analysis pipeline validation for upcoming cluster cosmology analyses such as the ones that will be performed by the Vera C. Rubin Legacy Survey of Space and Time-Dark Energy Science Collaboration (LSST-DESC). Its purpose is to serve as a flexible, easy-to-install, and easy-to-use interface for both weak lensing simulators and observers and can be applied to real and mock data to study the systematics affecting weak lensing mass reconstruction. At the core of CLMM are routines to model the weak lensing shear signal given the underlying mass distribution of galaxy clusters and a set of data operations to prepare the corresponding data vectors. The theoretical predictions rely on existing software, used as backends in the code, that have been thoroughly tested and cross-checked. Combined theoretical predictions and data can be used to constrain the mass distribution of galaxy clusters as demonstrated in a suite of example Jupyter Notebooks shipped with the software and also available in the extensive online documentation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Sequential multidimensional heteroepitaxy of chalcogen-sharing 3D ZnSe and 2D MoSe 2 with quasi van der Waals interface engineering

Two-dimensional (2D) materials are emerging as a promising platform for epitaxial growth, largely free from the constraints of lattice constant and thermal expansion coefficient mismatches. Among them, transition metal dichalcogenides (TMDs), known for their superior electrical properties, are ideal for ultrathin semiconductor applications. Their unique epitaxial characteristics enable seamless integration with 3D materials, facilitating the development of gate stacks and heterojunction devices. In this regard, developing a process for growing high-quality 3D epitaxial materials before and after the growth of 2D TMDs and understanding the 2D/3D interface are crucial. This study demonstrates the sequential growth of fully epitaxial ZnSe/MoSe 2 /ZnSe heterostructures using metal-organic chemical vapor deposition. ZnSe and MoSe 2 , sharing chalcogen elements, enable large-area quasi van der Waals epitaxy with sharp interfaces without intermediate phase. Multiscale analysis involving transmission electron microscopy and density functional theory calculation reveals lattice commensurability, van der Waals gaps, termination, and interfacial reconstruction. Understanding these interactions is crucial for advancing multidimensional integration of 2D and 3D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Application of machine learning interatomic potentials in heterogeneous catalysis

Heterogeneous catalysts are crucial in modern societies as they promote sustainability by enabling lower-energy pathways for various chemical reactions. While Density Functional Theory (DFT) computations can provide critical insights into how heterogeneous catalysts operate at the atomic level, they are limited by computational costs and unfavorable scaling with system size. Recently, machine learning interatomic potentials (MLIPs) have emerged as a promising alternative to DFT, offering near-DFT accuracy at significantly reduced cost. Here, in this perspective, we discuss the application of MLIPs in heterogeneous catalyst modeling as a surrogate for DFT. We detail how MLIPs have been applied in thermal catalysis to probe active sites, enable studying complex metallic and nanoporous catalysts, and investigate the reconstruction of catalytic surfaces. We review the use of MLIPs in electrocatalysis and photocatalysis, emphasizing their capabilities in studying transition metal oxide surfaces and solid–liquid interfaces. We also discuss the current limitations of MLIPs, particularly their challenges with transferability and description of non-local interactions. Finally, we conclude by identifying promising and underexplored domains in which MLIPs can further advance our understanding of heterogeneous catalysts.

Catalytic surfaces↗
DOE OSTI2023

Microscopic Origin of Polarization Charges at Ga N / ( Al , Ga ) N Interfaces

Ga⁢N/(Al,Ga)⁢N heterojunctions are at the heart of high-electron-mobility transistors that are being adopted for high-power and high-frequency applications. The strong polarization fields present at this interface significantly enhance the density of the two-dimensional electron gas (2DEG) that is confined on the Ga⁢ N side of the junction. The microscopic origin of these electrons has been debated over the years: after excluding that they would be contributed by bulk donors, a model that identifies surface states on the (Al, Ga)⁢N surface as the source of electrons has become widely adopted. Recently, it has become clear, however, that the measured density of surface states is insufficient to account for the high electron density in the 2DEG. Here we demonstrate, based on state-of-the-art first-principles calculations, that the source of electrons is intrinsic to the overall structure and that the negative charge in the 2DEG is balanced by fixed charge on the surface. We perform a rigorous study of polarization, using our recently developed methodology for quantifying polarization fields within the finite-sized systems that can be addressed with density-functional calculations. The results show that the electrons that appear in the 2DEG originate locally at the interface, and that the net charge at the interface is predominantly compensated by fixed charge on the surface, rather than surface states. We elucidate the source of this fixed charge and associate it with surface reconstructions or the presence of heterovalent impurities (such as oxygen). Our results force a reassessment of the impact of surface states on the density of the 2DEG: rather than serving as a supply of electrons, the surface states mainly act to pin the Fermi level. Our conclusions allow a fresh interpretation of experimental observations and allow devising guidelines for optimizing carrier densities in the 2DEG.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Deep learning of free boundary and Stefan problems

Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of free boundaries and complex dynamic interfaces. In this work, we propose a multi-network model based on physics-informed neural networks to tackle a general class of forward and inverse free boundary problems called Stefan problems. Specifically, we approximate the unknown solution as well as any moving boundaries by two deep neural networks. Besides, we formulate a new type of inverse Stefan problems that aim to reconstruct the solution and free boundaries directly from sparse and noisy measurements. We demonstrate the effectiveness of our approach in a series of benchmarks spanning different types of Stefan problems, and illustrate how the proposed framework can accurately recover solutions of partial differential equations with moving boundaries and dynamic interfaces. All code and data accompanying this manuscript are publicly available at https://github.com/PredictiveIntelligenceLab/DeepStefan.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

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

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host–pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum–Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral ( x-y) pixel resolution of 10 nm and an axial ( z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum–Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Biochemistry & Molecular Biology↗
DOE OSTI2024

Structural anisotropy in Sb thin films

Sb thin films have attracted wide interest due to their tunable band structure, topological phases, high electron mobility, and thermoelectric properties. We successfully grow epitaxial Sb thin films on a closely lattice-matched GaSb(001) surface by molecular beam epitaxy. We find a novel anisotropic directional dependence on their structural, morphological, and electronic properties. The origin of the anisotropic features is elucidated using first-principles density functional theory (DFT) calculations. The growth regime of crystalline and amorphous Sb thin films was determined by mapping the surface reconstruction phase diagram of the GaSb(001) surface under Sb 2 flux, with confirmation of structural characterizations. Crystalline Sb thin films show a rhombohedral crystal structure along the rhombohedral (211) surface orientation parallel to the cubic (001) surface orientation of the GaSb substrate. At this coherent interface, Sb atoms are aligned with the GaSb lattice along the [1̄10] crystallographic direction but are not aligned well along the [110] crystallographic direction, which results in anisotropic features in reflection of high-energy electron diffraction patterns, misfit dislocation formation, surface morphology, and transport properties. Our DFT calculations show that the preferential orientation of the rhombohedral Sb (211) plane may originate from the GaSb surface, where Sb atoms align with the Ga and Sb atoms on the reconstructed surface. The formation energy calculations confirm the stability of the experimentally observed structures. Our results provide optimal film growth conditions for further studies of novel properties of Bi 1-x Sb x thin films with similar lattice parameters and an identical crystal structure, as well as functional heterostructures of them with III–V semiconductor layers along the (001) surface orientation, supported by a theoretical understanding of the anisotropic film orientation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Monomer and dimer structures of cytochrome bo 3 ubiquinol oxidase from Escherichia coli

Abstract The Escherichia coli cytochrome bo 3 ubiquinol oxidase is a four‐subunit heme‐copper oxidase that serves as a proton pump in the E. coli aerobic respiratory chain. Despite many mechanistic studies, it is unclear whether this ubiquinol oxidase functions as a monomer, or as a dimer in a manner similar to its eukaryotic counterparts—the mitochondrial electron transport complexes. In this study, we determined the monomeric and dimeric structures of the E. coli cytochrome bo 3 ubiquinol oxidase reconstituted in amphipol by cryogenic electron microscopy single particle reconstruction (cryo‐EM SPR) to a resolution of 3.15 and 3.46 Å, respectively. We have discovered that the protein can form a dimer with C2 symmetry, with the dimerization interface maintained by interactions between the subunit II of one monomer and the subunit IV of the other monomer. Moreover, the dimerization does not induce significant structural changes in the monomers, except the movement of a loop in subunit IV (residues 67–74).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2024

Compliant Intramedullary Stems for Joint Reconstruction

The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Dynamic Behavior of Spatially Confined Sn Clusters and Its Application in Highly Efficient Sodium Storage with High Initial Coulombic Efficiency

Advanced battery electrodes require a cautious design of microscale particles with built-in nanoscale features to exploit the advantages of both micro- and nano-particles relative to their performance attributes. Herein, the dynamic behavior of nanosized Sn clusters and their host pores in carbon nanofiber) during sodiation and desodiation is revealed using a state-of-the-art 3D electron microscopic reconstruction technique. For the first time, the anomalous expansion of Sn clusters after desodiation is observed owing to the aggregation of clusters/single atoms. Pore connectivity is retained despite the anomalous expansion, suggesting inhibition of solid electrolyte interface formation in the sub-2-nm pores. Taking advantage of the built-in nanoconfinement feature, the CNF film with nanometer-sized interconnected pores hosting Sn clusters (≈2 nm) enables high utilization (95% at a high rate of 1 A g –1 ) of Sn active sites while maintaining an improved initial Coulombic efficiency of 87%. Finally, the findings provide insights into electrochemical reactions in a confined space and a guiding principle in electrode design for battery applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

TomocuPy – efficient GPU-based tomographic reconstruction with asynchronous data processing

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU–GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work with less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20–30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048 3 tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗
DOE OSTI2022

Engineering surface segregation of perovskite oxide through wet exsolution for CO catalytic oxidation

Cation segregation occurring near the surface or interfaces of solid catalysts plays an important role in catalytic reactions. Unfortunately, the native surface of perovskite oxides is dominated by passivated A-site segregation, which severely hampers the catalytic activity and durability of the system. To address this issue, we present a wet exsolution method to reconstruct surface segregation in perovskite cobalt oxide. Here, under reduction etching treatment of glycol solution, inert surface Sr segregation was transformed into active Co 3 O 4 segregation. By varying the reaction time, we achieved differing coverage of the active Co 3 O 4 segregation on the La 0.5 Sr 0.5 CoO 3-δ (LSCO) perovskite oxide surface. This study reveals that CO oxidation activity exhibits a volcano-shaped dependence on the coverage of Co 3 O 4 segregation at the surface of a perovskite cobalt oxide. Furthermore, we find that a suitable coverage of Co 3 O 4 segregation can dramatically improve the catalytic activity of the perovskite catalyst by enhancing interface interactions. Co K-edge, Co L-edge, and O K-edge X-ray absorption spectra confirm that the synergistic effect optimizes the covalence of the metal-oxygen bond at the surface and interface. This work not only contributes to the design of perovskite-type catalysts, but also provides important insight into the relationship between surface segregation and catalytic activity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Resolving the Evolution of Atomic Layer-Deposited Thin-Film Growth by Continuous In Situ X-Ray Absorption Spectroscopy

In situ synchrotron X-ray absorption near-edge structure characterization of thin-film titania growth by atomic layer deposition (ALD) over ZnO nanowires reveals persistent low-coordinated Ti motifs leading to a new picture of ALD growth. Through the design of growth and measurement cycles, Ti K-edge spectral data are continuously recorded so as to characterize the film evolution as a function of ALD cycle number and the surface changes within the time scale of the ALD cycle. A unified set of analysis tools is developed to interpret the time-series of spectral data. A prenucleation stage of growth, a transition region, and then a steady-state growth stage are observed with distinguishable features. Multivariate curve resolution analysis, that is physically constrained, demonstrates two specific spectral components with associated, time-dependent concentrations. The bulk-film component tracks the stages of growth. The surface and interface components, present throughout the stages of growth, reveal a significant coverage of relatively isolated or loosely networked tetrahedrally coordinated Ti atomic motifs. Lastly, spectral signatures for the intra-cycle growth kinetics are reconstructed at a time resolution of ~1 s and demonstrate that the transient Ti motifs on the growing surface stabilize within a few seconds of the Ti precursor pulse.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗