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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quasi-static to Dynamic Mechanical Response and Microstructure Development of Tantalum-Tungsten Alloys

Lawrence Livermore National Laboratory (LLNL) is interested the quasi-static to dynamic mechanical response and microstructure evolution of tantalum-tungsten (Ta-W) alloys (Ta-2.5W, Ta-5W, and Ta-10W, wt.%) made by conventional wrought processing and additive manufacturing (AM). This work scope was performed at the Colorado School of Mines (Mines) and included quasi-static (e.g., 10 -3 s -1 ) mechanical testing in tension and compression, along with selected high strain rate (Kolsky) pressure bar testing in compression (e.g., 10 3 s -1 ), with and without temperature variations in some instances. Complementary microstructure characterization was performed on undeformed and deformed samples to understand the role of processing on microstructural evolution and the deformation mechanisms that impact the mechanical response with variations in strain rate, temperature, and strain state (e.g., tension versus compression in selected examples). The wrought material provided by LLNL from Viridis Materials was found to have unrecrystallized regions within the microstructure, which led to unexpected results relative to previously reported properties for Ta-W. The AM Ta-2.5W (wt.%) material provided by LLNL was found to have higher compressive strength than wrought Ta-2.5W (wt.%), which is hypothesized to be due to differences in the crystallographic texture between the two materials. This project partially supported several postdocs and graduate students at Mines.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure-Based Understanding of High-Temperature Deformation Behaviors in Laser Powder Bed Fusion (LPBF) 316H Stainless Steel

This report presents the results of high-temperature mechanical property testing and microstructural analysis of laser powder bed fusion (LPBF) 316H stainless steel (SS) conducted during Fiscal Year 2025 (FY25) under the U.S. Department of Energy, Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program. The study builds upon prior work by expanding the mechanical property test matrix and advancing a microstructure-based mechanistic understanding of LPBF 316H SS performance, with a focus on the solution annealed (SA, 1100°C for 1 hour) materials. High-temperature tension, creep, fatigue, and creep-fatigue tests were performed. Thermal aging studies were conducted. Advanced characterization techniques, including scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), and high-energy synchrotron X-ray diffraction, were employed to investigate the microstructural evolution during thermal aging and mechanical testing. It was discovered that the as-built (AB) LPBF 316H SS exhibited precipitation kinetics during thermal aging that are 10–100 times faster than its wrought counterpart due to the presence of high densities of preferred nucleation sites, while the precipitation kinetics in SA LPBF 316H was under investigation. The accelerated precipitation of embrittling phases such as sigma phase in LPBF 316H SS compared to its wrought counterpart led to significant impacts on creep ductility. The influence of laser printing parameters and build orientations on fatigue and creep-fatigue performance was observed. In assessing the creep-fatigue performance, it was discovered that the SA LPBF 316H samples failed in less than 200 cycles under 595°C, 0.5% (strain amplitude) with a 60-minute hold time. This work establishes a mechanistic framework for predicting the long-term behavior of LPBF 316H SS, supporting its rapid qualification under the ASME BPVC. The findings contribute to the broader goal of enabling the deployment of advanced materials in next-generation nuclear energy systems. An outlook for FY26 work, including continued aging studies, mechanical testing, and microstructural characterization, is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2024

Additive manufacturing of AISI M2 tool steel by binder jetting (BJ): Investigation of microstructural and mechanical properties

The presented research demonstrates for the first time the successful processing of AISI M2 tool steel by binder jetting, a promising additive manufacturing technique capable of producing complex shapes with minimal residual stresses and isotropic properties. The optimal printing parameters were explored by varying processing parameters such as the binder saturation (45 %–105 %), binder set time (0 to 10 s), targeted bed temperature (50–60 °C), oscillator (2600–2750 rpm), recoater (20–28 mm/s), and roller speeds (200–300 rpm). Microstructural characterization and evaluation of mechanical properties of binder jetted parts were performed using x-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) to study their chemical composition, powder morphology, microstructure, carbide morphologies, relative density, hardness, compressive strength, and ductility. Two powder sizes (5 and 10 μm) were used, and sintering was performed at varying temperatures (1270, 1280, and 1300 °C) and durations (60 and 120 min), followed by a furnace, air, and water cooling. An optimum hardness of ~970 HV was obtained when parts were sintered at 1270 °C for 60 min, followed by water quenching. Impressive compressive strength of ~ 3580 MPa was observed in the sample sintered at 1280 °C for 60 min duration, followed by air cooling. Furnace-cooled parts showed the highest density of ~95 %, whereas the relative density of air- and water-cooled parts varied between ~91 to 93.50 %, respectively. The microstructure of sintered samples revealed the formation of M 6 C stable carbide, M 2 C metastable carbide, MC as a secondary carbide, and a-Fe matrix, which contributed to the observed increase in mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Laser‐Powder Melt Pool Solidification Dynamics and Microstructural Engineering of Ti‐5553 Microlattices

The fine geometric and topological control afforded by additive manufacturing technologies has enabled the manufacture of architected materials across length scales, and enabling tunable mechanical performance as a function of local and global design. Progress has been made to tune the mechanical response of architected materials through geometry, but understanding how the geometry and processing conditions will inform the microstructure remains a challenge due to the rapid solidification in laser powder bed fusion. This study uses in situ X-ray imaging and electron backscatter diffraction microscopy to demonstrate that the melt pool size, microstructure morphology, and elastic strain distribution is influenced by a combination of lattice geometry and laser processing conditions. These results indicate that within larger melt pools the local thermal gradients are sufficient to enable a columnar-to-equiaxed transition across the melt pool. Furthermore, the solidification mechanisms producing these microstructures are examined across the first 5 ms of melting and solidification, described via in situ high-speed X-ray imaging and mirrored via multiphysics simulation.

additive manufacturing↗
DOE OSTI2025

Reactive Carbide‐Based Synthesis and Microstructure of NASICON Sodium Metal All Solid‐State Electrolyte

Reactive carbide precursor-based synthesis of NASICON-type NZSP (Na 1+x Zr 2 Si x P 3-x O 12 ) solid-state electrolyte (SSE) is demonstrated, in contrast to the established oxide-based approach. Exothermic decomposition of ZrC and SiC in air homogenizes microstructure, yielding 98% compact density after conventional sintering at 1200 °C. Quantitative stereology demonstrates that significant microstructural differences are present. Compacts of carbide-derived Carb-NZSP are 98% dense with a secondary zirconium oxide (ZrO 2 ) volume fraction of 0.2% ± 0.3%, versus 93% dense and 3% ± 1% for oxide-derived baseline. For Carb-NZSP, the secondary glassy phosphate phase is agglomerated, while for baseline, it is dispersed and percolated. Electrochemical testing combined with post-mortem analysis demonstrates how microstructural control of secondary phases is critical for dendrite suppression: Carb-NZSP critical current density (CCD) is 3.1 ± 0.8 mA cm −2 at 0.1 mAh cm −2 , versus 1.0 ± 0.7 mA cm −2 at 0.1 mAh cm −2 . Cryogenic focused ion beam (cryo-FIB) analysis demonstrates that in both materials, the porous 2D sheet-like sodium metal dendrites propagate around and subsume NZSP grains, likely following a path enriched with glassy phase and with porosity. Dendrites also flow around isolated zirconia particles. Phase field simulation reveals deflection of dendrites by mechanically tough zirconia, while brittle glassy phase accelerates dendrite growth, especially when finely distributed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of laser melt schedule on the microstructure of additively manufactured IN718 Superalloy

Laser powder bed fusion (L-PBF) has enabled the fabrication of geometrically complex metallic structures and components that are challenging to producing using conventional manufacturing approaches. The site-specific and far from equilibrium thermal conditions of L-PBF offer the potential to facilitate multi-length scale design of structure and properties across the atomic-through macro-levels. However, L-PBF systems face scalability challenges due to throughput constraints. Laser rotary powder bed fusion (L-RPBF) systems are being investigated as a solution to enhance the deposition rates compared to conventional L-PBF. Rotary systems also offer additional flexibility for controlling the time structure of melting through laser interleaving on alternating layers. Here, in this study, IN718 test samples were printed using single-laser or interleaved dual-laser configuration in a L-RPBF system to investigates the effect of process settings and melt-interleaving on as-fabricated microstructure. The microstructural evolution, such as grain size and crystallographic texture, was assessed by determining variations in the melt-pool shapes. Laser interleaving leads to a reduction in average grain size compared to single laser by ∼ 40 % at high power (400 W) and by ∼36 % at medium power (370 W). Results presented here identify key challenge for obtaining uniform microstructures and barriers for the broader adoption of high-deposition rate L-RPBF.

Dual-laser↗
DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Gas tungsten arc welding and post weld heat treatment effects on microstructure and mechanical property of castable nanostructured alloy steel

Here, this paper details the first study of castable nanostructured alloy (CNA) steel gas tungsten arc weldability and the post-weld heat treatment (PWHT) effects. Effects of welding heat input, thermal cycles, and PWHT on microstructures, microhardness distributions, room temperature tensile properties, and fracture characteristics are discussed. Results show that CNA steel exhibits excellent weldability (i.e., no indication of welding defects and reasonable tensile properties). The welded joint exhibited heterogeneous microstructures with δ-ferrite as well as large microhardness variation and fluctuation. The welded joint yield and ultimate tensile strengths were similar to those of the base metal, but the elongations decreased by 30 %. However, with normalization and tempering PWHT, the δ-ferrite was eliminated, microstructure was modified, hardness was unified, and joint ductility was restored. The study indicated that the CNA reduced-activation ferritic-martensitic steel owns excellent weldability, and PWHT is needed for the industrial application of welded structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural evolution and phase stability in Nb-containing interstitial Fe-Mn-Co-Cr-C high-entropy alloys: An in-situ synchrotron X-ray diffraction study during laser melting

The influence of Nb on phase stability and microstructural evolution in an interstitial Fe-Mn-Co-Cr-C high-entropy alloy was investigated using in-situ synchrotron X-ray diffraction (SXRD) during laser melting. Scheil-Gulliver simulations predict the formation of σ and γ-f.c.c. phases in all three alloys, along with NbC in Nb-containing compositions. SXRD confirmed the presence of most predicted phases, but the σ phase was absent. Nb promotes crystallite refinement and increases dislocation density, though excessive additions reduce refinement efficiency due to solubility limits and secondary phase formation. Furthermore, Nb addition also enhances ε-h.c.p. phase formation by reducing stacking fault energy through NbC-induced carbon depletion. Analysis of intensity peak evolution reveals that Nb alters preferred grain orientations, reducing {111} γ intensity while enhancing {220} γ , leading to a more isotropic grain distribution. Overall, Nb plays a key role in phase selection, microstructure refinement, and preferred orientation evolution, allowing the tailored microstructure of high-entropy alloys via rapid solidification.

Interstitial high entropy alloys↗
DOE OSTI2025

Investigation of MgO additives on microstructure and properties of thin LLZO electrolytes for all-solid-state batteries

To realize high-energy density lithium lanthanum zirconate (LLZO)-based solid-state batteries (SSB), LLZO electrolytes should be fabricated with low thickness and high mechanical strength. An effective strategy for strengthening ceramic materials is to use additives. Here, we employed MgO nanopowders and fibers as additives for the thin LLZO electrolyte in order to improve the mechanical strength. The microstructure, mechanical properties, and electrochemical properties are characterized to investigate the effects of adding MgO and sintering time. The MgO remains at grain boundaries after sintering, making the microstructure of LLZO fine and uniform. The mechanical strength of the MgO-added LLZO was enhanced by more than 60% while maintaining high ionic conductivity (1 × 10 -4 S cm -1 ) at room temperature. Li symmetric cells using the MgO fiber–LLZO and MgO powder–LLZO exhibit 2 and 3 times higher critical current density (CCD) than those of pure LLZO, and a solid-state full cell exhibits stable cycling performance. Further, these results demonstrate that the use of MgO nanopowder or fiber as an additive for thin LLZO is beneficial for high-current density cycling, by improving mechanical properties and microstructure.

25 ENERGY STORAGE↗
DOE OSTI2025

SOC Microstructural Analyzer

This program was designed to analyze the 3-phase microstructure of the electrodes of a solid oxide fuel cell (SOFC) or electrolysis cell (SOEC), both referred to in combination as a solid oxide cell (SOC). It is agnostic to the exact system, so it could be repurposed to analyze any 3-phase microstructure. This tool directly analyzes segmented voxel-based data that has been segmented into phase IDs (1,2,3). The voxels will be analyzed directly for: - tortuosity factors - triple phase boundaries - 2-phase interfacial areas, using a meshed isosurface - mean diameters of each phase, using an inscribed sphere method - standard deviation of the diameters of each phase, from the same inscribed sphere data - connectivity information Comprehensive information is available in the readme file (within the zipped repository in Markdown language, and also available here as a rendered PDF). Please cite this page / DOI, as well as https://doi.org/10.1111/jace.14775, for usage.

3D microstructure↗
DOE OSTI2026

Optimization of Processing, Microstructure, and Hardness of an Al–Ce–Ni–Mn–Zr Alloy With Laser Additive Manufacturing

Here, this study examines the processing behavior, microstructure, surface roughness, and hardness properties of an aluminum alloy containing 8.2 Ce, 4.5 Ni, 0.5 Mn, and 0.7 Zr (wt%) fabricated using laser powder bed fusion. Sixty samples were produced across a range of laser powers, scan speeds, and hatch spacings to evaluate their effect on porosity, hardness, and microstructural features. Porosity was measured using X-ray computed tomography, while microstructure and surface roughness were characterized by scanning electron (SEM) and laser confocal microscopy. High dense and cracking-free Al–Ni–Ce alloy was successfully manufactured. Porosity showed a U-shaped dependence on energy input, increasing under both insufficient and excessive melting conditions. Hardness increased with cooling rate due to finer cellular structures and solute redistribution. A general statistical model was developed to capture the relationships between processing parameters and material response. Results identify a narrow processing window defined by laser powers between 350 and 370 W, scan speeds from 1400 to 1800 mm/s, and hatch distances between 0.14 and 0.18 mm. Within this window, porosity is minimized (below 0.01%) and hardness is maximized (up to 160 HV), demonstrating that careful control of these parameters enables dense, high strength aluminum components suitable for demanding structural applications.

Aluminum alloys↗
DOE OSTI2025

Microstructure‐Dependent Sodium Storage Mechanisms in Hard Carbon Anodes

Sustainable energy storage is essential to support the transition to renewables and meet the increasing demand for energy. Sodium‐ion batteries (NIBs) are attractive for grid‐scale energy storage due to the abundance and low cost of sodium, sustainability of other battery components, and electrochemical performance. Hard carbon (HC) is a leading anode material for NIBs, but its complex microstructure complicates the understanding of sodium storage mechanisms. Using X‐ray total scattering and density functional theory calculations, this study clarifies how HC's microstructural variations influence sodium storage across the slope (high potential) and plateau (low potential) regions of the potential capacity curve. In the slope region, sodium initially adsorbs at high‐binding energy defect sites and subsequently intercalates between graphene layers, adsorbing at low‐binding energy defect sites, correlating with different slopes observed during initial sodiation. Initial irreversibility arises from sodium trapping at surface defects and solid electrolyte interface formation. In the plateau region, sodium simultaneously intercalates and fills pores, influenced by pore size, interlayer spacing, and defect concentration. HCs with larger pore sizes form larger sodium clusters. In conclusion, the proposed mechanism underscores the role of microstructure engineering in enhancing HC performance and advancing NIBs for grid‐scale energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗