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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Influence of Cryomilling on Crystallite Size of Aluminum Powder and Spark Plasma Sintered Component

The present investigation aims to develop nanocrystalline (NC) pure aluminum powders using cryomilling technique and manufacture bulk components using spark plasma sintering (SPS). The cryomilling was performed on pure Al powders for 2, 6, and 8 h. The cryomilled powders were then consolidated using SPS to produce bulk components. The particle morphology and crystallite size of the powders and the bulk SPS components were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The results showed that the crystallite size of pure Al powders decreases with increased cryomilling time. The results also showed that the SPS at elevated temperatures resulted in a slight increase in crystallite size, however, the changes were insignificant. The mechanical properties of the bulk components were determined using a Vickers microhardness tester. The hardness of the cryomilled SPS component was determined to be three times higher than that of the unmilled SPS component. The mechanism for the reduction in crystallite size with increasing cryomilling time is discussed. This fundamental study provides an insight into the development of bulk nanomaterials with superior mechanical properties for automotive, aerospace, marine, and nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Activation and surface reactions of CO and H 2 on ZnO powders and nanoplates under CO hydrogenation reaction conditions

Here, activation and surface reactions of CO and H 2 on ZnO powders and nanoplates under CO hydrogenation reaction conditions were (quasi) in situ studied using temperature programmed surface reaction spectra, diffuse reflectance Fourier transform infrared spectroscopy, inelastic neutron scattering spectroscopy and electron paramagnetic resonance. CO undergoes disproportion reaction to produce gaseous CO 2 and surface carbon adatoms, and adsorbs to form surface formate species. H 2 adsorption forms dominant irreversibly-adsorbed surface hydroxyl groups and interstitial H species and very minor surface Zn-H species. Surface formate species and hydroxyl groups react to produce CO 2 and H 2 , while surface carbon adatoms are hydrogenated by surface Zn-H species sequentially to produce CH(a), CH 2 (a), CH 3 (a) and eventually gaseous CH 4 . The ZnO nanoplates, exposing a higher fraction of Zn-ZnO(0001) and O-ZnO(000–1) polar facets, are more active than the ZnO powders to catalyze CO hydrogenation to CH 4 . These results provide fundamental understanding of the reaction mechanisms and structural effects of CO hydrogenation reaction catalyzed by ZnO-based catalysts.

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

Baseline Graphite Characterization

Baseline Graphite Characterization: Purpose of Baseline, Mechanical Baseline Tests, Physical Properties Tests, Methodology, Baseline progress, Density, Ultimate Tensile Strength, PCEA Ultimate Tensile Strength, Split Disc Tests, Compression Tests, Flexural Tests, and Coefficient of Thermal Expansion.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Resolving Nanoscale Processes during Carbon Mineralization Using Identical Location Transmission Electron Microscopy

Basalt reservoirs offer the potential for carbon mineralization, aiding in achieving net-zero emissions. However, debates persist about microscopic crystallization mechanisms due to limited characterization techniques under high-temperature and pressure conditions. By using Identical Location Transmission Electron Microscopy (IL-TEM) and cryo-TEM, this study reveals nanoscale interfacial carbonation processes of forsterite and diopside nanoparticles in water-saturated supercritical carbon dioxide under realistic reservoir conditions. Further, both minerals undergo preferential metal cation dissolution into a thin water film, forming porous Si-rich amorphous layers, supporting the leached layer mechanism as the dominant mineral reactivity process. Diopside’s amorphous layer has lower porosity and growth rate relative to forsterite, likely related to the connectivity of silicate tetrahedra. Kinetically favorable nesquehonite and aragonite nanocrystals form on the amorphous layers. These findings support the development of accurate reservoir simulations and help enable commercial-scale carbon storage deployment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Advanced Characterization of Thin Film Solar Cells

Polycrystalline thin-film solar cells have reached a levelized cost of energy that is competitive with all other sources of electricity. The technology has significantly improved in recent years, with laboratory cell efficiencies for cadmium telluride (CdTe), perovskites, and copper indium gallium diselenide (CIGS) each exceeding 22 percent. Both CdTe and CIGS solar panels are now produced at the gigawatt scale. However, there are ongoing challenges, including the continued need to improve performance and stability while reducing cost. Advancing polycrystalline solar cell technology demands an in-depth understanding of efficiency, scaling, and degradation mechanisms, which requires sophisticated characterization methods. These methods will enable reseachers and manufacturers to improve future solar modules and systems. This work provides researchers with a concise overview of the status of thin-film solar cell technology and characterization. Chapters describe material systems and their properties and then provide an in-depth look at relevant characterization methods and the learning facilitated by each of these. Following an introductory chapter, the book provides systematic and thorough coverage of the following topics: trends to improve CdTe solar cell performance; Cu(In,Ga)Se2 and related materials; perovskite solar cells; photovoltaic device modelling; luminescence and thermal imaging of thin-film photovoltaic materials, devices, and modules; application of spatially resolved spectroscopy characterization techniques on Cu2ZnSnSe4 solar cells; time-resolved photoluminescence characterization of polycrystalline thin-film solar cells; fundamentals of electrical material and device spectroscopies applied to thin-film polycrystalline chalcogenide solar cells; nanometer-scale characterization of thin-film solar cells by atomic force microscopy-based electrical probes; scanning transmission electron microscopy characterization of solar cells; photoelectron spectroscopy methods in solar cell research; time-of-flight secondary-ion mass spectrometry and atom probe tomography; and solid-state nuclear magnetic resonance characterization for photovoltaic applications. The final chapter provides an overview and describes future prospects.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

Characterization of Large Batch 14YWT Heat (NFA-2) for JOYO Irradiations (Rev.1)

The development and deployment of advanced cladding materials in advanced reactors requires the collection of relevant performance data for each cladding concept. Novel cladding concepts typically undergo significant innovation and iteration over the course of their development, and intention should be given to providing a snapshot of present performance. Thus, a modern heat of the nanostructured ferritic alloy (NFA) 14YWT is produced using state-of-the-art procedures for impurity minimization combined with thermomechanical processing methods designed to optimize fracture toughness, tensile strength, and ductility. This new heat of 14YWT, NFA-2, will provide the Advanced Fuels Campaign with adequate material for upcoming irradiation and mechanical testing campaigns. This report outlines procurement of new gas atomized 14YWT powder, production of four new bars of 14YWT, and subsequent mechanical and microstructural property characterization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Micropillar Compression of Additively Manufactured 316L Stainless Steels after 2 MeV Proton Irradiation: A Comparison Study between Planar and Cross-Sectional Micropillars

A micropillar compression study with two different techniques was performed on proton-irradiated additively manufactured (AM) 316L stainless steels. The sample was irradiated at 360 °C using 2 MeV protons to 1.8 average displacement per atom (dpa) in the near-surface region. A comparison study with mechanical test and microstructure characterization was made between planar and cross-sectional pillars prepared from the irradiated surface. While a 2 MeV proton irradiation creates a relatively flat damage zone up to 12 µm, the dpa gradient by a factor of 2 leads to significant dpa uncertainty along the pillar height direction for the conventional planar technique. Cross-sectional pillars can significantly reduce such dpa uncertainty. From one single sample, three cross-sectional pillars were able to show dpa-dependent hardening. Furthermore, post-compression transmission electron microscopy allows the determination of the deformation mechanism of individual micropillars. Cross-sectional micropillar compression can be used to study radiation-induced mechanical property changes with better resolution and less data fluctuation.

transmission electron microscopy↗
DOE OSTI2020

Build orientation, surface roughness, and scan path influence on the microstructure, mechanical properties, and flexural fatigue behavior of Ti–6Al–4V fabricated by electron beam melting

In the present work, microstructure and fatigue behavior of Electron Beam Melted (EBM) Ti–6Al–4V have been systematically studied. Fatigue behavior was investigated using four-point bend tests of rectangular bars with internal features fabricated with different build orientations, scan paths, and surface conditions. Microstructural variations were evaluated by optical microscopy and Vickers micro-hardness indentations, both of which were found to be consistent throughout the build and within ranges reported in the literature. The fatigue performance of the EBM material was comparable to wrought Ti–6Al–4V when the loading direction was parallel to the build direction, but severely limited when loaded perpendicular to the build direction or if surface roughness defects were present. Scanning electron microscopy and interrupted fatigue tests were utilized to characterize fracture surfaces, failure mechanisms, crack initiation, and propagation behavior. Crack initiation sites in the vertically-oriented specimens were identified to be melt-related defects formed by insufficient melting.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Establishment of a Vertically Integrated Domestic Manufacturing Process for Production of Substrates Needed for Manufacture of Gas Diffusion Layers

In this project, AvCarb, LLC evaluated the baseline performance metrics of commercial carbon veils and their corresponding Gas Diffusion Layers (GDLs) with the goal of establishing an optimized, vertically integrated production system for wet-laid nonwoven substrates used in gas diffusion media for electrochemical energy storage and conversion devices. Mechanical testing and microstructural characterization were conducted and used to develop a multiscale computational model capable of simulating and predicting the performance of GDLs in fuel cells. Although the project successfully generated foundational transport and modeling data, it was terminated prior to identifying the critical GDL design parameters necessary for full optimization. The program aimed to improve carbon veil fabrication through enhanced fiber dispersion, fiber-fiber adhesion control, and improved web formation, enabling the production of high-quality, uniform substrates. Simulations were intended to guide mixing and solution delivery system design and process conditions, followed by production-scale trials to evaluate fiber dispersion, web uniformity, and mechanical robustness. At full deployment, the proposed production line would have been capable of producing approximately 650,000 m² of carbon veil annually. This capability remains strategically important, as the United States currently lacks a domestic source of wet-laid nonwoven carbon substrates that satisfy the stringent quality requirements for fuel-cell GDLs and electrolyzers representing an ongoing supply-chain vulnerability. Beyond supply-chain benefits, the project established a robust benchmarking dataset for existing commercial carbon veils while advancing next-generation material concepts targeting improved performance and manufacturing consistency.

Olson, Cynthia Lemay↗
DOE OSTI2020

Temperature- and Rate-Dependent Pathways in Formation of Metastable Silicon Phases under Rapid Decompression

High-pressure metallic beta-Sn silicon (Si-II), depending on temperature, decompression rate, stress, etc., may transform to diverse metastable forms with promising semiconducting properties under decompression. However, the underlying mechanisms governing the different transformation paths are not well understood. Here, two distinctive pathways, viz., a thermally activated crystal-crystal transition and a mechanically driven amorphization, were characterized under rapid decompression of Si-II at various temperatures using in situ time-resolved x-ray diffraction. Under slow decompression, Si-II transforms to a crystalline bc8/r8 phase in the pressure range of 4.3-9.2 GPa through a thermally activated process where the overdepressurization and the onset transition strain are strongly dependent on decompression rate and temperature. In comparison, Si-II collapses structurally to an amorphous form at around 4.3 GPa when the volume expansion approaches a critical strain via rapid decompression beyond a threshold rate. The occurrence of the critical strain indicates a limit of the structural metastability of Si-II, which separates the thermally activated and mechanically driven transition processes. The results show the coupled effect of decompression rate, activation barrier, and thermal energy on the adopted transformation paths, providing atomistic insight into the competition between equilibrium and nonequilibrium pathways and the resulting metastable phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Non -degenerate marginal-likelihood calibration with application to quantum characterization

Here, we propose a marginal likelihood strategy within the Kennedy-O’Hagan (KOH) Bayesian framework, where a Gaussian process (GP) models the discrepancy between a physical system and its simulator. Our approach introduces a novel marginalized likelihood by integrating out the degenerate eigenspace of the covariance matrix, rather than approximating the original likelihood. Unlike approximation methods that compromise accuracy for computational efficiency, our method defines an exact likelihood—distinct from the original but preserving all relevant information. This formulation achieves computational efficiency and stability, even for large datasets where the covariance matrix nears degeneracy. Applied to the characterization of a superconducting quantum device at Lawrence Livermore National Laboratory, the approach enhances the predictive accuracy of the Lindblad master equations for modeling Ramsey measurement data by effectively quantifying uncertainties consistent with the quantum data.

general physics↗
DOE OSTI2023

Probing the Mechanical Performance of Micro-architected Porous Structures Through In Situ Characterization and Analysis

Micropores play critical roles in both natural and man-made materials. Such pores take on a variety of shapes and sizes ranging from spherical to irregular sphere-like voids with diameters spanning from the nanometer to millimeter scales. When porous structures are mechanically loaded, the pores direct the stress around their free surfaces, altering the material’s mechanical response relative to fully dense materials. In this study, for the first time, we create micro-architected porous samples using nanolithography and investigate the role of pore morphology by conducting a series of in situ micropillar compression tests in scanning electron microscopy. The findings demonstrate that porosity is the primary factor influencing the mechanical response of these micro-architected materials, as often seen on a macroscopic level. Additionally, we observed that pore geometry had a significant impact on Young’s modulus, yield stress, and strain energy density as a secondary parameter. Then, the extracted Young’s modulus was compared to macroscopic empirical models and determined the analytical models sufficiently described the impact of porosity in the microscopic scale but failed to capture the impact of second-order parameters. Furthermore, these results suggest how porous materials can be tailored to achieve desired mechanical properties based on the engineering applications of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Separate-Effects Tests for Studying Temperature-Gradient-Driven Cracking in UO 2 Pellets

We report a variety of normal operation and accident scenarios can generate thermal stresses large enough to cause cracking in light-water reactor (LWR) fuel pellets. Cracking of fuel pellets can lead to reduced heat removal, higher centerline temperatures, and localized stress in cladding, all of which impact fuel performance. It is important to experimentally characterize the thermal and mechanical behavior in the pellet before and after cracking to improve cracking models in fuel performance codes. However, in-reactor observation and measurement of cracking is very challenging due to the harsh environment and logistics. Recently, an experimental pellet cracking test stand was developed for separate effects testing of normal operations and accident temperature conditions, using thermal imaging to capture the pellet surface temperature for evaluation of thermal stresses and optical imaging to capture the evolution of cracking in real time. Experiments were performed using depleted uranium dioxide (UO 2 ) pellets, which are useful for collecting data valuable for development and validation of cracking models. A combination of induction and resistance heating was used to create an average temperature gradient of 236°C/cm and 193°C/cm before and after cracking respectively. Characterization of the pellets were done before as well as after cracking. The cracking patterns are somewhat different than those expected in a typical reactor because of the differences in thermal conditions and pellet microstructure. However, if the actual conditions of these experiments are reproduced in computational models, these out-of-pile tests on UO 2 pellets provide relevant data for modeling purposes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Quantification of microtubule stutters: dynamic instability behaviors that are strongly associated with catastrophe

Microtubules (MTs) are cytoskeletal fibers that undergo dynamic instability (DI), a remarkable process involving phases of growth and shortening separated by stochastic transitions called catastrophe and rescue. Dissecting DI mechanism(s) requires first characterizing and quantifying these dynamics, a subjective process that often ignores complexity in MT behavior. We present a Statistical Tool for Automated Dynamic Instability Analysis (STADIA) that identifies and quantifies not only growth and shortening, but also a category of intermediate behaviors that we term “stutters.” During stutters, the rate of MT length change tends to be smaller in magnitude than during typical growth or shortening phases. Quantifying stutters and other behaviors with STADIA demonstrates that stutters precede most catastrophes in our in vitro experiments and dimer-scale MT simulations, suggesting that stutters are mechanistically involved in catastrophes. Related to this idea, we show that the anticatastrophe factor CLASP2γ works by promoting the return of stuttering MTs to growth. STADIA enables more comprehensive and data-driven analysis of MT dynamics compared with previous methods. The treatment of stutters as distinct and quantifiable DI behaviors provides new opportunities for analyzing mechanisms of MT dynamics and their regulation by binding proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effects of Ligand Chemistry on Ion Transport in 2D Hybrid Organic–Inorganic Perovskites

2D hybrid organic–inorganic perovskites are potentially promising materials as passivation layers that can enhance the efficiency and stability of perovskite photovoltaics. The ability to suppress ion transport is proposed as a stabilization mechanism, yet an effective characterization of relevant modes of halide diffusion in 2D perovskites is nascent. In light of this knowledge gap, molecular dynamics simulations with enhanced sampling and experimental validation to systematically characterize how ligand chemistry in seven (R-NH 3 ) 2 PbI 4 systems impacts halide diffusion, particularly in the out-of-plane direction is combined. It is found that increasing stiffness and length of ligands generally inhibits ion transport, while increasing ligand polarization generally enhances it. Structural and energetic analyses of the migration pathways provide quantitative explanations for these trends, which reflect aspects of the disorder of the organic layer. Overall, this mechanistic analysis greatly enhances the current understanding of halide migration in 2D hybrid organic–inorganic perovskites and yields insights that can inform the design of future passivation materials.

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

Mesoscale Linear Elastic Modeling and Homogenization of Marine Energy Composites

The design of fiber-reinforced composite (FRC)-based components for marine energy applications necessitates a fundamental understanding of material properties and the resulting geometry to predict long-term performance. In this work, we present a modeling workflow to predict linear elastic and diffusive bulk properties at the mesoscale for an idealized geometry based on knowledge of fiber and resin properties. A parametric study was performed to identify the key model input parameters that influence bulk properties. Furthermore, we demonstrate how bulk properties can be leveraged in high-fidelity image-based simulations, where imperfections in tow geometry and voids captured during X-ray computed tomography imaging are explicitly represented within the simulation. Bulk properties of interest include moduli, Poisson’s ratios, hygroscopic swelling, diffusivity, and moisture uptake, which are key parameters for characterizing FRC performance within marine environments. Modeling predictions agreed well with experimental data, except for estimating swelling coefficients, likely due to crack accumulation as a function of moisture uptake. The mesoscale modeling workflow ultimately highlights a versatile framework for understanding the influence of material and geometric properties, which can be leveraged to rapidly assess new FRC-based components.

computational mechanics↗
DOE OSTI2021

Classification of orthostatic intolerance through data analytics

Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. Here we use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

60 APPLIED LIFE SCIENCES↗