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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Effect of heat input on the microstructure, residual stresses and corrosion resistance of 304L austenitic stainless steel weldments

Austenitic stainless steels are widely used in high performance pressure vessels, nuclear, chemical, process and medical industry due to their very good corrosion resistance and superior mechanical properties. However, austenitic stainless steels are prone to sensitization when subjected to higher temperatures (673 K to 1173 K) during the manufacturing process (e.g. welding) and/or certain applications (e.g. pressure vessels). During sensitization, chromium in the matrix precipitates out as carbides and intermetallic compounds (sigma, chi and Laves phases) decreasing the corrosion resistance and mechanical properties. In the present investigation, 304L austenitic stainless steel was subjected to different heat inputs by shielded metal arc welding process using a standard 308L electrode. The microstructural developments were characterized by using optical microscopy and electron backscattered diffraction, while the residual stresses were measured by X-ray diffraction using the sin{sup 2}ψ method. It was observed that even at the highest heat input, shielded metal arc welding process does not result in significant precipitation of carbides or intermetallic phases. The ferrite content and grain size increased with increase in heat input. The grain size variation in the fusion zone/heat affected zone was not effectively captured by optical microscopy. This study shows that electron backscattered diffraction is necessary to bring out changes in the grain size quantitatively in the fusion zone/heat affected zone as it can consider twin boundaries as a part of grain in the calculation of grain size. The residual stresses were compressive in nature for the lowest heat input, while they were tensile at the highest heat input near the weld bead. The significant feature of the welded region and the base metal was the presence of a very strong texture. The texture in the heat affected zone was almost random. - Highlights: • Effect of heat input on microstructure, residual stresses and corrosion is studied. • HAZ and width of dendrite in the welded region increase with heat input. • Residual stresses are tensile near the welded region after the highest heat input. • Welded region has the highest pit density after highest heat input. • Dendrites and δ-ferrite were highly oriented in the welded region.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Synthesis and characterization of visible-active molybdenum disulfide (2H-MoS{sub 2}) nanospheres

In current study, a novel 2H-MoS{sub 2} nanospheres were successfully synthesized and underwent structural- as well as optical-property characterizations. The MoS{sub 2} were prepared by one pot hydrothermal approach through adopting L-cysteine as environmentally-benignchalcogenide precursor. TEM image shows that the as-synthesized MoS{sub 2} appear to be spherical in shape with size distribution in the range of 120 nm – 180 nm. HRTEM lattice-fringes imaging further elucidate that the interlayer spacing at the edges is equal to be 0.62 nm that correspond to (002) plane stacking. Also, the HRTEM image clearly-illustrate that the internal microstructure of MoS{sub 2} composed of randomly-arrayed alternating layers, which render the postulation that the formation of nanosphere is driven by self-assembly of individual layers into globular morphology. XRD diffractogram that appear to be broad and unresolved reveal the partially crystalline nature of the sample. Optical-absorption spectra depicts the sample is visible active with featureless absorption, which can attribute to indirect transition of the excitions generated. By using Tauc plot, the bandgap energy is determined to be 1.75 eV, which reflect the nanospheres are indeed visible-active nanostructures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

Shape- and phase-controlled ZnS nanostructures and their optical properties

Graphical abstract: (a) TEM images of the nanorods, the HRTEM images for the lower (b) and the upper (c) part of the rod in (a). - Highlights: • Stacking faults were observed for ZnS nanocrystals with the size of ∼5 nm. • Nanotwinning structures and stacking faults were observed in ZnS nanorod. • Microstructure defects were found to be formed randomly for nanocrystals and nanorods. • The 1LO phonon mode exhibits a red-shift of 6 cm{sup −1} as the particle size increases from 5 to 15 nm. - Abstract: Single-crystalline ZnS nanoparticles with a zinc-blende crystal structure have some microdefects such as stacking faults and nanotwins. In contrast, ZnS nanorods have a wurtzite crystal structure, which grows along the [0 0 0 1] direction, although some nanorods display the intergrowth of a minor zinc-blende phase and the major wurtzite phase, which forms stacking faults or zinc-blende/wurtzite ZnS nanotwins. Raman spectroscopy measurements reveal surface phonons and longitudinal optical phonons in the nanoparticles, nanorods and doublet phonons that are associated with the transversal optical phonons of the A1 and E1 modes in only the nanorods. The first-order longitudinal optical phonon mode exhibits a blueshift of 6 cm{sup −1} when the particle size increases from 5 to 15 nm, but there is no shift in the range of 15–30 nm because of quantum confinement and microdefects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding interfacial segregation in polymer blend films with random and mixed side chain bottlebrush copolymer additives

Bottlebrush polymers are complex macromolecules with tunable physical properties dependent on the chemistry and architecture of both the side chains and the backbone. Prior work has demonstrated that bottlebrush polymer additives can be used to control the interfacial properties of blends with linear polymers but has not specifically addressed the effects of bottlebrush side chain microstructures. Here, using a combination of experiments and self-consistent field theory (SCFT) simulations, we investigated the effects of side chain microstructures by comparing the segregation of bottlebrush additives having random copolymer side chains with bottlebrush additives having a mixture of two different homopolymer side chain chemistries. Specifically, we synthesized bottlebrush polymers with either poly(styrene-ran-methyl methacrylate) side chains or with a mixture of polystyrene (PS) and poly(methyl methacrylate) (PMMA) side chains. Furthermore, the bottlebrush additives were matched in terms of PS and PMMA compositions, and they were blended with linear PS or PMMA chains that ranged in length from shorter to longer than the bottlebrush side chains. Experiments revealed similar behaviors of the two types of bottlebrushes, with a slight preference for mixed side-chain bottlebrushes at the film surface. SCFT simulations were qualitatively consistent with experimental observations, predicting only slight differences in the segregation of bottlebrush additives driven by side chain microstructures. Specifically, these slight differences were driven by the chemistries of the bottlebrush polymer joints and side chain end-groups, which were entropically repelled and attracted to interfaces, respectively. Using SCFT, we also demonstrated that the interfacial behaviors were dominated by entropic effects with high molecular weight linear polymers, leading to enrichment of bottlebrush near interfaces. Surprisingly, the SCFT simulations showed that the chemistry of the joints connecting the bottlebrush backbones and side chains played a more significant role compared with the side chain end groups in affecting differences in surface excess of bottlebrushes with random and mixed side chains. This work provides new insights into the effects of side chain microstructure on segregation of bottlebrush polymer additives.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity simulations of solidification microstructures are typically performed using multicomponent partial differential equations (PDEs) with moving interfaces. The inherent randomness of the PDE initial conditions (grain seeds) necessitates ensemble simulations to predict microstructure statistics, e.g., grain size, aspect ratio, and crystallographic orientation. Here, currently such ensemble simulations are prohibitively expensive and surrogates are necessary.In GrainGNN, we use a dynamic graph to represent interface motion and topological changes due to grain coarsening. We use a reduced representation of the microstructure using hand-crafted features; we combine pattern finding and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both networks have an encoder-decoder architecture; the encoder has a multi-layer transformer long-short-term-memory architecture; the decoder is a single layer perceptron.We evaluate GrainGNN by comparing it to high-fidelity phase field simulations for in-distribution and out-of-distribution grain configurations for solidification under laser power bed fusion conditions. GrainGNN results in 80%–90% pointwise accuracy; and nearly identical distributions of scalar quantities of interest (QoI) between phase field and GrainGNN simulations compared using Kolmogorov-Smirnov test. GrainGNN's inference speedup (PyTorch on single x86 CPU) over a high-fidelity phase field simulation (CUDA on a single NVIDIA A100 GPU) is 150×–2000× for 100-initial grain problem. Further, using GrainGNN, we model the formation of 11,600 grains in 220 seconds on a single CPU core.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Experimental and computational analysis of bending fatigue failure in chopped carbon fiber chip reinforced composites

With a better balance among good mechanical performance, high freedom of design, and low material and manufacturing cost, chopped carbon fiber chip reinforced sheet molding compound (SMC) composites show great potential in different engineering applications. Here in this paper, bending fatigue behaviors of SMC composites considering the heterogeneous fiber orientation distributions have been thoroughly investigated utilizing both experimental and computational methods. First, four-point bending fatigue tests are performed with designed SMC composites, and the local modulus is adopted as a metric to represent the local fiber orientation of two opposing sides. Interestingly, SMC composites with and without large discrepancy in local modulus of opposing sides show different fatigue behaviors. Interrupted tests are conducted to explore the bending fatigue failure mechanism, and the damage processes of valid specimens are also closely examined. We find that the fatigue failure of SMC composites under four-point bending is governed by crack propagation instead of crack initiation. Because of this, the heterogeneous local fiber orientations of both sides of the specimen influence fatigue life. The microstructure of the lower side shows a direct influence while that of the upper side also exhibiting influence which becomes more prominent for high cycle fatigue cases. Furthermore, a hybrid micro–macro computational model is proposed to efficiently study the cyclic bending behavior of SMC composites. The region of interest is reconstructed with a modified random sequential absorption algorithm to conserve all the microstructural details including the heterogeneous fiber orientation, while the rest of the regions are modeled as homogenized macro-scale continua. Combined with a framework to capture the progressive fatigue damage under cyclic bending, the bending fatigue behaviors of SMC composites are accurately captured by the hybrid computational model comparing with our experimental analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Structural, electrical, and thermoelectric properties of bismuth telluride: Silicon/carbon nanocomposites thin films

In this study, the effect of the presence of secondary phases on the structural, electrical, and thermoelectric properties of nanocomposite Bi{sub 2}Te{sub 3} films prepared by co-sputtering of silicon and carbon with Bi{sub 2}Te{sub 3} has been investigated. Growth temperature and the presence of Si and C phase are observed to have a strong effect on the topography and orientation of crystallites. X-ray diffraction study demonstrates that Bi{sub 2}Te{sub 3} and Bi{sub 2}Te{sub 3}:C samples have preferred (0 0 15) orientation in comparison to Bi{sub 2}Te{sub 3}:Si sample, which have randomly oriented crystallites. Atomic force, conducting atomic force, and scanning thermal microscopy analysis show significant differences in topographical, electrical, and thermal conductivity contrasts in Bi{sub 2}Te{sub 3}:Si and Bi{sub 2}Te{sub 3}:C samples. Due to the randomly oriented crystallites and the presence of Si along the crystallite boundaries, appreciable Seebeck coefficient, higher electrical conductivity, and lower thermal conductivity is achieved resulting in relatively higher value of power factor (3.71 mW K{sup −2} m{sup −1}) for Bi{sub 2}Te{sub 3}:Si sample. This study shows that by incorporating a secondary phase along crystallite boundaries, microstructural, electrical, and thermoelectric properties of the composite samples can be modified.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Microstructural characterization of weld joints of 9Cr reduced activation ferritic martensitic steel fabricated by different joining methods

This paper presents a detailed electron microscopy study on the microstructure of various regions of weldment fabricated by three welding methods namely tungsten inert gas welding, electron beam welding and laser beam welding in an indigenously developed 9Cr reduced activation ferritic/martensitic steel. Electron back scatter diffraction studies showed a random micro-texture in all the three welds. Microstructural changes during thermal exposures were studied and corroborated with hardness and optimized conditions for the post weld heat treatment have been identified for this steel. Hollomon–Jaffe parameter has been used to estimate the extent of tempering. The activation energy for the tempering process has been evaluated and found to be corresponding to interstitial diffusion of carbon in ferrite matrix. The type and microchemistry of secondary phases in different regions of the weldment have been identified by analytical transmission electron microscopy. - Highlights: • Comparison of microstructural parameters in TIG, electron beam and laser welds of RAFM steel • EBSD studies to illustrate the absence of preferred orientation and identification of prior austenite grain size using phase identification map • Optimization of PWHT conditions for indigenous RAFM steel • Study of kinetics of tempering and estimation of apparent activation energy of the process.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Statistical scaling of geometric characteristics in stochastically generated pore microstructures

In this study, we analyze the statistical scaling of structural attributes of virtual porous microstructures that are stochastically generated by thresholding Gaussian random fields. Characterization of the extent at which randomly generated pore spaces can be considered as representative of a particular rock sample depends on the metrics employed to compare the virtual sample against its physical counterpart. Typically, comparisons against features and/patterns of geometric observables, e.g., porosity and specific surface area, flow-related macroscopic parameters, e.g., permeability, or autocorrelation functions are used to assess the representativeness of a virtual sample, and thereby the quality of the generation method. Here, we rely on manifestations of statistical scaling of geometric observables which were recently observed in real millimeter scale rock samples [13] as additional relevant metrics by which to characterize a virtual sample. We explore the statistical scaling of two geometric observables, namely porosity (Φ) and specific surface area (SSA), of porous microstructures generated using the method of Smolarkiewicz and Winter [42] and Hyman and Winter [22]. Our results suggest that the method can produce virtual pore space samples displaying the symptoms of statistical scaling observed in real rock samples. Order q sample structure functions (statistical moments of absolute increments) of Φ and SSA scale as a power of the separation distance (lag) over a range of lags, and extended self-similarity (linear relationship between log structure functions of successive orders) appears to be an intrinsic property of the generated media. The width of the range of lags where power-law scaling is observed and the Hurst coefficient associated with the variables we consider can be controlled by the generation parameters of the method.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Metallographic Characterization of Wrought Depleted Uranium

Metallographic characterization was performed on wrought depleted uranium (DU) samples taken from the longitudinal and transverse orientations from specific locations on two specimens. Characterization of the samples included general microstructure, inclusion analysis, grain size analysis, and microhardness testing. Comparisons of the characterization results were made to determine any differences based on specimen, sample orientation, or sample location. In addition, the characterization results for the wrought DU samples were also compared with data obtained from the metallographic characterization of cast DU samples previously characterized. No differences were observed in microstructure, inclusion size, morphology, and distribution, or grain size in regard to specimen, location, or orientation for the wrought depleted uranium samples. However, a small difference was observed in average hardness with regard to orientation at the same locations within the same specimen. The longitudinal samples were slightly harder than the transverse samples from the same location of the same specimen. This was true for both wrought DU specimens. Comparing the wrought DU sample data with the previously characterized cast DU sample data, distinct differences in microstructure, inclusion size, morphology and distribution, grain size, and microhardness were observed. As expected, the microstructure of the wrought DU samples consisted of small recrystallized grains which were uniform, randomly oriented, and equiaxed with minimal twinning observed in only a few grains. In contrast, the cast DU microstructure consisted of large irregularly shaped grains with extensive twinning observed in most grains. Inclusions in the wrought DU samples were elongated, broken and cracked and light and dark phases were observed in some inclusions. The mean inclusion area percentage for the wrought DU samples ranged from 0.08% to 0.34% and the average density from all wrought DU samples was 1.62E+04/cm 2 . Inclusions in the cast DU samples were equiaxed and intact with light and dark phases observed in some inclusions. The mean inclusion area percentage for the cast DU samples ranged from 0.93% to 1.00% and the average density from all wrought DU samples was 2.83E+04/cm 2 . The average mean grain area from all wrought DU samples was 141 μm 2 while the average mean grain area from all cast DU samples was 1.7 mm2. The average Knoop microhardness from all wrought DU samples was 215 HK and the average Knoop microhardness from all cast DU samples was 264 HK.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Microstructure evolution during low-strain thermo-mechanical processing and its repercussion on intergranular corrosion in alloy 600H

Highlights: • GBE microstructure in alloy 600H is realized through the sole activation of SIBM. • Higher TRD size and lower fractal dimension are characteristics of GBE microstructure. • Partially/fully recrystallized specimen shows higher DOS than as-received condition. • GBE specimen shows remarkable resistance against percolation and grain dropping. • Extent of percolation is governed by residual strain and random HAGBs connectivity. - Abstract: The current investigation deals with the evolution of microstructures as a consequence of varying processing parameters viz. extent of deformation, annealing temperature and time during grain boundary engineering (GBE) type thermo-mechanical processing in alloy 600H. Furthermore, the influence of the various product microstructures (viz. partially recrystallized, fully recrystallized and GBE) on intergranular corrosion is evaluated and compared with the as-received condition. Detailed characterization of the as-processed specimens have revealed that lower extent of deformation (7.5%) followed by a longer annealing treatment (i.e. 60 min) at a higher temperature (1373 K) could lead to a GBE microstructure with a higher proportion of Σ3{sup n} boundaries (~72%) due to the predominant activation of multiple twinning. In contrast, the specimens associated with moderately higher extent of deformation (10% and 15%) have revealed the occurrence of static recrystallization. The GBE specimen has demonstrated an excellent resistance against percolation essentially due to its discontinuous random high angle boundaries (HAGBs) network. In contrast, the nucleation of the finer grains in the recrystallized specimens caused the regeneration of random HAGBs network (as substantiated by the fractal analysis) that leads to higher percolation and severe grain dropping. In spite of the similar grain boundary character distribution, the severity of corrosion is relatively lesser in the as-received condition than the completely recrystallized specimen owing to its coarse-grained microstructure that commendably retarded the diffusion of chromium from the grain interior to the boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Text Mining for Process–Structure–Properties Relationships in Metals

With the advent of large language models (LLMs), the vast unstructured text within millions of academic papers is increasingly accessible for materials discovery—although significant challenges remain. While LLMs offer promising few- and zero-shot learning capabilities, particularly valuable in the materials domain where expert annotations are scarce, general-purpose LLMs often fail to address key materials-specific queries without further adaptation. To bridge this gap, fine-tuning LLMs on human-labeled data is essential for effective structured knowledge extraction (Liu in The Importance of Human-Labeled Data in the Era of LLMs, 2023). Here, in this study, we introduce a novel annotation schema designed to extract generic process–structure–properties relationships from scientific literature. We demonstrate the utility of this approach using a dataset of 128 abstracts, with annotations drawn from two distinct domains: high-temperature materials (Domain I) and uncertainty quantification in simulating materials microstructure (Domain II). Initially, we developed a conditional random field (CRF) model based on MatBERT—a domain-specific BERT variant—and evaluated its performance on Domain I. Subsequently, we compared this model with a fine-tuned LLM (GPT-4o from OpenAI) under identical conditions. Our results indicate that fine-tuning LLMs can significantly improve entity extraction performance over the BERT-CRF baseline on Domain I. However, when additional examples from Domain II were incorporated, the performance of the BERT-CRF model became comparable to that of the GPT-4o model. These findings underscore the potential of our schema for structured knowledge extraction and highlight the complementary strengths of both modeling approaches.

Materials science↗
DOE OSTI2014

Stoichiometry determined exchange interactions in amorphous ternary transition metal oxides: Theory and experiment

Amorphous transition metal oxides exhibit exotic transport and magnetic properties, while the absence of periodic structure has long been a major obstacle for the understanding of their electronic structure and exchange interaction. In this paper, we have formulated a theoretical approach, which combines the melt-quench approach and the spin dynamic Monte-Carlo simulations, and based on it, we explored amorphous Co{sub 0.5}Zn{sub 0.5}O{sub 1−y} ternary transition metal oxides. Our theoretical results reveal that the microstructure, the magnetic properties, and the exchange interactions of Co{sub 0.5}Zn{sub 0.5}O{sub 1−y} are strongly determined by the oxygen stoichiometry. In the oxygen-deficient sample (y > 0), we have observed the long-range ferromagnetic spin ordering which is associated with the non-stoichiometric cobalt-rich region rather than metallic clusters. On the other hand, the microstructure of stoichiometric sample takes the form of continuous random networks, and no long-range ferromagnetism has been observed in it. Magnetization characterization of experimental synthesized Co{sub 0.61}Zn{sub 0.39}O{sub 1−y} films verifies the relation between the spin ordering and the oxygen stoichiometry. Furthermore, the temperature dependence of electrical transport shows a typical feature of semiconductors, in agreement with our theoretical results.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

COBRA:COMPUTED-TOMOGRAPHY BASED RANDOM-FIELD APPROXIMATION

SF-25-115 COBRA (COmputed-tomography Based Random-field Approximation) is a Python application for generating statistically equivalent random fields from CT-scan imagery. It leverages Karhunen–Loève expansions to model microstructural variability, enabling users to: Preprocess CT scans (filtering and Gaussian transformation); Fit covariance kernels fromempirical data; Solve eigenproblems to obtain KL modes; Sample random fields onsistent with fitted statistics; Postprocess samples back into the physical domain.

Hu, Tianchen↗
DOE OSTI2012

Extracting E Versus K Effective Band Structure from Supercell Calculations on Alloys and Impurities

The supercell approach to defects and alloys has circumvented the limitations of those methods that insist on using artificially high symmetry, yet this step usually comes at the cost of abandoning the language of E versus k band dispersion. Here we describe a computational method that maps the energy eigenvalues obtained from large supercell calculations into an effective band structure (EBS) and recovers an approximate E(k) for alloys. Making use of supercells allows one to model a random alloy A{sub 1-x}B{sub x}C by occupying the sites A and B via a coin-toss procedure, affording many different local environments (polymorphic description) to occur. We present the formalism and implementation details of the method and apply it to study the evolution of the impurity band appearing in the dilute GaN:P alloy. We go beyond the perfectly random case, realizing that many alloys may have nonrandom microstructures, and investigate how their formation is reflected in the EBS. It turns out that the EBS is extremely sensitive in determining the critical disorder level for which delocalized states start to appear in the intermediate band. In addition, the EBS allows us to identify the role played by atomic relaxation in the positioning of the impurity levels.

36 MATERIALS SCIENCE↗
DOE OSTI2016

3D Representative Volume Element Reconstruction of Fiber Composites via Orientation Tensor and Substructure Features

To provide a seamless integration of manufacturing processing simulation and fiber microstructure modeling, two new stochastic 3D microstructure reconstruction methods are proposed for two types of random fiber composites: random short fiber composites, and Sheet Molding Compounds (SMC) chopped fiber composites. A Random Sequential Adsorption (RSA) algorithm is first developed to embed statistical orientation information into 3D RVE reconstruction of random short fiber composites. For the SMC composites, an optimized Voronoi diagram based approach is developed for capturing the substructure features of SMC chopped fiber composites. The proposed methods are distinguished from other reconstruction works by providing a way of integrating statistical information (fiber orientation tensor) obtained from material processing simulation, as well as capturing the multiscale substructures of the SMC composites.

Li, Yi↗