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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Microstructure tuning facilitated photo-efficiency enhancement and environmental benign nature of HfO2/Mo/HfO2 multilayer films

Nano-layered, multilayered ?lms consisting of HfO2(dielectric)/Mo(metal)/HfO2(dielectric), (D/M/D archi-tecture) with controlled layer thickness, chemistry and microstructure were demonstrated for e?cient utiliza-tion of solar energy with well-designated spectral control. The e?ect of Mo metal interlayer on the energy performance and e?ciency of HfO2/Mo/HfO2 multilayer ?lms made by electron-beam deposition onto silicon and glass substrates has been evaluated. The Mo interlayer thickness [Mo(t)] was varied in the range of 5–25 nm while the top/bottom HfO2 layer thickness was kept constant at ~50 nm. Structural, morphological and optical characterization was performed to understand the e?ect of Mo(t) induced microstructure on mechanical dur-ability and optical behavior of the D/M/D multilayer ?lms. Structural studies revealed that D/M/D multilayer ?lms with relatively low Mo(t) were amorphous, while higher Mo(t) induces the amorphous-to-nanocrystalline microstructure transformation, which also induces surface roughening e?ects. The D/M/D multilayer ?lm with Mo(t) = 20 nm, where the nano-columnar morphology of the D-M-D layers are aligned perpendicular to the substrate surface and exhibits the optimum spectral selectivity. Such microstructure and morphological tailoring also facilitates the environmentally friendly (self-cleaning/stain repellent) nature of these multilayer ?lms by the high contact angle (~102°) achieved. Molybdenum interlayer facilitated higher contact angle leads to super-hydrophobic nature of the HfO2/Mo/HfO2 multilayered ?lm surfaces. The low surface roughness values (0.2–0.8 nm), which are primarily due to Mo at the interface, of the HfO2/Mo/HfO2 multilayered ?lms satisfy the general requirements of heat mirror applications. In addition, the microstructure of intermediate Mo layer and HfO2-Mo interfaces signi?cantly control the mechanical properties of D/M/D multilayer ?lms. Optimal me-chanical properties (hardness = 30 GPa, elastic modulus = 312 GPa, wear resistance = 0.094, adhesion strength = 2050 µN) have been obtained for Mo(t) = 20 nm D/M/D multilayer ?lm. The mechanical response strongly supports ?exible and wear resistive nature of HfO2/Mo/HfO2 multilayer ?lms.

Dubey, P↗
DOE OSTI2018

Co-dependent microstructural evolution pathways in metastable d-ferrite in cast austenitic stainless steels during thermal aging

Cast austenitic stainless steels (CASS) are excellent alloys because they combine high corrosion resistance with high strength and toughness. However, they are susceptible to embrittlement upon long-term thermal aging at elevated temperatures. Thus, the microstructural evolution pathways during thermal aging need to be understood to predict and potentially prevent embrittlement. Atom probe tomography was used to identify and quantify the microstructural evolution pathways of the d-ferrite in different CASS alloys aged for up to 10,000 hours at temperatures between 290 °C and 400 °C. The four steels – CF8, CF8M, CF3, and CF3M – which vary by Mo and C concentration, each experienced spinodal decomposition of the d-ferrite, and precipitation of G-phase clusters and Cu clusters attached to the G-phase. There were large differences in the extent of these features due to their Mo and C concentration. Using radial distribution function analysis, the interactions of constituent elements was found to determine the evolution of these features, with Mo and C specifically influencing the movement of Cr, Ni, Si, Mn, and Cu atoms due to their relative miscibility with these elements. The results will help inform predictive models for the use of duplex stainless steels for extended operation at high temperatures.

Duplex stainless steel, Aging, Spinodal decomposit↗
DOE OSTI2018

Microstructural Evolution and Mechanical Properties of Ti-22Al-25Nb (At.%) Orthorhombic Alloy with Three Typical Microstructures

Microstructural evolution, tensile and creep behavior of Ti-22Al-25Nb (at.%) orthorhombic alloy with three typical microstructures were investigated. The three typical microstructures were obtained by different solution and age treatment temperatures and analyzed by the BSE technique. The tensile strengths of the alloy at room temperature and 650 °C were investigated. The creep behaviors of the three typical microstructures were also studied at 650 °C/150 MPa for 100 h in air. The phase transformation mechanisms in creep deformation were also found. The experimental results showed that the formations of the three typical microstructures were decided by the isothermal forging and heat treatment. It was supposed that the high-temperature solution treatment might be dominant for the volume fraction and diameter of the equiaxed particles. While the double age treatment would lead to lamellar O phases. Due to grain refinement strengthening, the equiaxed microstructure presented the best tensile strength and ductility. The fully lamellar microstructure had the best creep resistance than that of other microstructures. In this paper, the phenomenon of creep-induced α{sub 2} phase decomposition was occurred during creep deformation of the equiaxed microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Phase transformations, microstructural refinement and defect evolution mechanisms in Al-Si alloys under non-hydrostatic diamond anvil cell compression

Non-hydrostatic compression of materials using a diamond anvil cell (DAC) can transform equilibrium microstructure of an alloy to novel, potentially metastable states. In this study, in situ synchrotron X-ray diffraction (XRD) during compression up to 24 GPa and subsequent ex situ, high resolution analytical electron microscopy (AEM) after decompression of an Al-Si alloy provided insight into the crystallographic changes during the compression as well as microstructural refinement, and defect structures caused by such a high pressure compression-decompression process. Pressure resolved in-situ synchrotron XRD was used to detail the phase transformation pathway of the eutectic Si phase in Al-Si alloy, from Si-I → Si-XI → Si-V during compression, and a final transformation predominantly to Si-III after decompression. Using scanning and transmission electron microscopy (S/TEM), site specific analysis of the alloy immediately underneath the anvil contact surface demonstrated a highly complex microstructure. A narrow region of thick amorphous Al oxide interspersed with nanocrystalline grains was found at the top surface. Underneath this Al oxide, while the majority the eutectic Si was transformed into highly-deformed, polycrystalline (PC) Si-III, a complex intermediate layer was discovered at the interface between Al and Si, comprised of a small fraction of Al nanocrystals and a majority of nanocrystalline Si-I. This combination of pressure resolved in-situ synchrotron XRD coupled with subsequent high resolution, electron microscopy resolved the phase transformation as well as non-equilibrium microstructures in a metallic alloy induced by a non-hydrostatic high pressure compression followed by decompression.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding the microstructural stability in a y’-strengthened Ni-Fe-Cr-Al-Ti alloy

Ni-Fe-Cr-Al-Ti alloys, with Ni levels near 50 wt.%, have the potential to develop a microstructure consisting of a face-centered cubic ? matrix with the homogeneous precipitation of fine ordered ?’ precipitates similar to traditional Ni-based superalloys with significantly greater Ni content. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), atom probe tomography (APT), and CALPHAD -based thermodynamic modeling were employed to understand the phase stabilities and microstructural evolution in an age-hardenable Ni-27Fe-18Cr-1Co-1.6Al-3.75Ti-1.2Mo-0.03C (wt%) alloy. The primary heat-treatment of solution annealing at 1121°C for 4h and age-hardening treatment at 760 °C for 16h resulted in a microstructure consisting of fine ?’ precipitates in an austenitic matrix along with grain boundary carbides, consistent with thermodynamic calculations. Long-term aging at 900 °C for 250h resulted in the coarsening of ?’ along with a change in the morphology of the precipitates from spherical to a more cuboidal shape. In addition, ? phase formation was observed concomitant with the partial dissolution of the ?’ phase. The ability of computational thermodynamic models to predict microstructural characteristics is discussed.

Gwalani, Bharat↗
DOE OSTI2018

Reduced-order microstructure-sensitive protocols to rank-order the transition fatigue resistance of polycrystalline microstructures

We present that the transition fatigue regime between low cycle fatigue (LCF) and high cycle fatigue (HCF) is often addressed in the design and performance evaluation of load-bearing components used in many structural applications. Transition fatigue is characterized by elevated levels of local inelastic deformation in significant regions of the microstructure as compared to HCF. Typically, crystal plasticity finite element method (CPFEM) simulations are performed to model this phenomenon and to rank-order microstructures by their resistance to crack formation and early growth in the regime of transition fatigue. Unfortunately, these approaches require significant computational resources, inhibiting their use to explore novel materials for transition fatigue resistance. Reduced-order, microstructure-sensitive models are needed to accelerate the search for next-generation, fatigue-resistant materials. In a recent study, Paulson et al. (2018) extended the materials knowledge system (MKS) framework for rank-ordering the HCF resistance of polycrystalline microstructures. The efficacy of this approach lies in the reduced-dimensional representation of microstructures through 2-point spatial correlations and principal component analysis (PCA), in addition to the characterization of the HCF response with a small set of performance measures. In this work, these same protocols are critically evaluated for their applicability to rank-order the transition fatigue resistance of the same class of polycrystalline microstructures subjected to increased strain amplitudes. Success in this endeavor requires the formation of homogenization linkages that account for the significantly higher levels of local inelastic deformation and stress redistribution in transition fatigue as compared to HCF. Finally, a set of 12 α-titanium microstructures generated using the open access DREAM.3D software (Groeber and Jackson, 2014) are employed for this evaluation.

2-point correlations↗
DOE OSTI2021

Multimodal Analysis of Spatially Heterogeneous Microstructural Refinement and Softening Mechanisms in Three-Pass Friction Stir Processed Al4Si Alloy

Multiple thermally and thermomechanically induced microstructural refinement mechanisms can be activated in metallic alloys when subjected to solid phase processing methods such as friction stir processing (FSP). In this work, we provide detailed descriptions of the relationship between region-specific microstructural refinement mechanisms and the variation in microhardness, through a systematic and multimodal microstructural characterization of an FSP-processed 75% cold-rolled Al-4 at.% Si model binary alloy. Spatially resolved high-energy synchrotron X-ray diffraction, electron backscattered diffraction, and scanning transmission electron microscopy were used to understand the spatially heterogeneous microstructural evolution due to FSP. Results provide insights into how mechanisms such as static recovery, static recrystallization, dynamic recovery and recrystallization, geometric and continuous dynamic recrystallization, and particle-stimulated static or dynamic grain nucleation may occur heterogeneously in the microstructure as a function of the distance from the stir zone in processed alloys, directly influencing the degree of softening. The systematic analysis of microstructures and hardness in the FSP-processed model binary alloy given in this work highlights the rich microstructural domains that can be uniquely harnessed through solid phase processing of metallic alloys.

Al4Si, Friction Stir Processing, Geometric Dynamic↗
DOE OSTI2023

Interface microstructure effects on dynamic failure behavior of layered Cu/Ta microstructures

Abstract Structural metallic materials with interfaces of immiscible materials provide opportunities to design and tailor the microstructures for desired mechanical behavior. Metallic microstructures with plasticity contributors of the FCC and BCC phases show significant promise for damage-tolerant applications due to their enhanced strengths and thermal stability. A fundamental understanding of the dynamic failure behavior is needed to design and tailor these microstructures with desired mechanical responses under extreme environments. This study uses molecular dynamics (MD) simulations to characterize plasticity contributors for various interface microstructures and the damage evolution behavior of FCC/BCC laminate microstructures. This study uses six model Cu/Ta interface systems with different orientation relationships that are as- created, and pre-deformed to understand the modifications in the plasticity contributions and the void nucleation/evolution behavior. The results suggest that pre-existing misfit dislocations and loading orientations (perpendicular to and parallel to the interface) affect the activation of primary and secondary slip systems. The dynamic strengths are observed to correlate with the energy of the interfaces, with the strengths being highest for low-energy interfaces and lowest for high-energy interfaces. However, the presence of pre-deformation of these interface microstructures affects not only the dynamic strength of the microstructures but also the correlation with interface energy.

42 ENGINEERING↗
DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗
DOE OSTI2026

ML-based Micro-CT SOFC Microstructure Models (from Kent 2026 Microstructural Augmentation paper)

Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')

3D microstructure↗
DOE OSTI2026

Quantifying Microstructure Variability in Laser Powder Bed Fusion 316 L Stainless Steel Microstructures with Spatial Statistics

Here, we have explored data-driven methods for material microstructure quantification that improve sensitivity to microstructural changes compared to traditional approaches. The methods integrate multiple microstructural properties, including grain morphology, crystallographic orientation, and material phase information. The simpler method employs maps of the Euclidean distance transformation metric to evaluate the morphology of grain boundary networks. The more intensive approach employs generalized spherical harmonic mapping for crystallographic orientations, per-pixel phase information, and a variational auto-encoder for dimensionality reduction and results in a multidimensional clustering of by microstructure similarity. Applied to an experimental dataset of additively manufactured steel, both methods detected slight variations in samples produced under nominally identical processing conditions. Both methods were able to distinguish between samples from multiple (nominally identical) builds, while the generalized spherical harmonics-based method could additionally cluster data samples rotated at two orientations on the build plate. The improved sensitivity of the methods, demonstrated through comparison with traditional microstructure characterization techniques, offers advantages for microstructure quantification and comparisons in advanced manufacturing applications.

SS316L↗
DOE OSTI2018

Importance of filter’s microstructure in dynamic filtration modeling of gasoline particulate filters (GPFs): Inhomogeneous porosity and pore size distribution

The state-of-the-art multiscale modeling of GPFs including channel scale, wall scale, and pore scale is described. The microstructures of two GPFs were experimentally characterized. The pore size distributions of the GPFs were determined by mercury porosimetry. The porosity was measured by X-ray computed tomography (CT) and found to be inhomogeneous across the substrate wall. The significance of pore size distribution with respect to filtration performance was analyzed. The predictions of filtration efficiency were improved by including the pore size distribution in the filtration model. A dynamic heterogeneous multiscale filtration (HMF) model was utilized to simulate particulate filtration on a single channel particulate filter with realistic particulate emissions from a spark-ignition direct-injection (SIDI) gasoline engine. The dynamic evolution of filter’s microstructure and macroscopic filtration characteristics including mass- and number-based filtration efficiencies and pressure drop were predicted and discussed. The microstructure of the GPF substrate including inhomogeneous porosity and pore size distribution is found to significantly influence local particulate deposition inside the substrate and macroscopic filtration performance and is recommended to be resolved in the filtration model to simulate and evaluate the filtration performance of GPFs.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

Comparing non-destructive 3D X-ray computed tomography with destructive optical microscopy for microstructural characterization of fiber reinforced composites

Due to their lightweight, relatively high stiffness properties, and formability into complex shapes, discontinuous fiber composites are advantageous for producing small and medium size components. Improved characterization techniques and post-processing methodologies are required for more reliable quantification of the microstructure and defect distributions in these materials, in order to employ model-based approaches to assess their structural integrity. This work compares a non-destructive X-ray approach with a destructive optical microscopy approach for characterizing the microstructural attributes, specifically the fiber volume fraction, porosity volume fraction, fiber orientation distribution, and fiber length distribution of discontinuous glass fibers in a polypropylene matrix. Additionally, a method for destructively determining the ambiguous components of the orientation tensor (related to the sign ambiguity of the out-of-plane angle in a destructive cross-sectional cut of a fiber) over a large surface area is included. It was found that fiber volume fraction and average fiber aspect ratio matched well, while fiber orientation and porosity detection had small but notable differences. The differences in the detection capabilities of each technique are quantified and discussed shedding light on the specific advantages and disadvantages of each approach, and enabling engineers to quantify uncertainty in their microstructural characterization measurements especially as they relate to model based structural integrity activities.

Non-destructive testing↗