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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In-Situ Study of Microstructure Evolution of Spinodal Decomposition in an Al-Rich High-Entropy Alloy

High-entropy alloys (HEAs) are materials which leverage the entropy of mixing to motivate the formation of single-phase solid solutions, even of immiscible elements. While these materials are well-recognized for their application to structural engineering, there is increasing interest in the use of HEAs for functional applications such as memory storage and energy devices. The current work investigates the HEA Al 1.3 CoCrCuFeNi, which has been previously shown to be single-phase at high temperatures, but undergoes phase separation at lower temperatures, transforming the structural and the functional properties. This phase separation is investigated at high temperatures with in-situ small angle neutron scattering (SANS) and scanning transmission electron microscopy (EDS). These techniques show that increasing the temperature up to 800°C, the microstructure of the HEA adiabatically disorders and abruptly homogenizes near 700°C, which is consistent with spinodal decomposition. Overall, the microstructural evolution proceeds mainly by the atomistic redistribution of the constituent elements within simple crystal lattices, producing coherent phase mixtures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Direct evidence of microstructure dependence of magnetic flux trapping in niobium

Abstract Elemental type-II superconducting niobium is the material of choice for superconducting radiofrequency cavities used in modern particle accelerators, light sources, detectors, sensors, and quantum computing architecture. An essential challenge to increasing energy efficiency in rf applications is the power dissipation due to residual magnetic field that is trapped during the cool down process due to incomplete magnetic field expulsion. New SRF cavity processing recipes that use surface doping techniques have significantly increased their cryogenic efficiency. However, the performance of SRF Nb accelerators still shows vulnerability to a trapped magnetic field. In this manuscript, we report the observation of a direct link between flux trapping and incomplete flux expulsion with spatial variations in microstructure within the niobium. Fine-grain recrystallized microstructure with an average grain size of 10–50 µm leads to flux trapping even with a lack of dislocation structures in grain interiors. Larger grain sizes beyond 100–400 µm do not lead to preferential flux trapping, as observed directly by magneto-optical imaging. While local magnetic flux variations imaged by magneto-optics provide clarity on a microstructure level, bulk variations are also indicated by variations in pinning force curves with sequential heat treatment studies. The key results indicate that complete control of the niobium microstructure will help produce higher performance superconducting resonators with reduced rf losses 1 related to the magnetic flux trapping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Long-term strength retrogression of silica-enriched oil well cement: A comprehensive multi-approach analysis

The strength retrogression of a Class G cement enriched by adding 60–80% silica cured under the condition of 200 °C and 50 MPa were investigated by multiple different analysis methods. Short-term strength analysis suggests sonic strength testing is a poor indicator of real mechanical strength at such curing condition. Long-term testing up to 142 d shows all designed systems experiences dramatic deterioration in physical and mechanical properties, such as compressive strength, Young's modulus, water permeability and gas permeability. Further testing was conducted using X-ray diffraction, thermal gravimetric analysis, mercury intrusion and scanning electron microscope. The study reveals that the set cement experiences significant microstructure coarsening with increasing curing time, especially after 30 d curing. The strength retrogression and microstructure coarsening seem to be accompanied with the continued consumption of silica, and are likely caused by the gradual conversion of semi-crystalline C-S-H into crystalline tobermorite and xonolite over the long-term curing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ Atomic-Scale Revelation of Amorphous Metallic Iron Formation during Hydrogen-Driven Reduction of Iron Oxides

The transition to hydrogen as a green reductant in metal production is critical for decarbonizing the metallurgical industry, yet atomic-scale mechanisms governing reduction pathways and phase evolution remain unresolved. Using in-situ environmental transmission electron microscopy, we identify a hidden pathway that reveals dynamic formation of amorphous metallic iron (Fe) during the hydrogen-driven reduction of ferrous oxides of Fe 3 O 4 and FeO. Real-time imaging uncovers three coexisting transformation routes: (i) Fe 3 O 4 → FeO, (ii) Fe 3 O 4 → amorphous Fe, and (iii) FeO → amorphous Fe. The resulting amorphous Fe exhibits fluid-like mobility, enabling its rapid aggregation and crystallization into core-shell nanostructures, with a crystalline core enveloped by an amorphous shell. Complementary ab initio molecular dynamics simulations trace the amorphous Fe formation to interfacial strain at the metal/oxide interfaces, where large lattice mismatches destabilize the metal lattice during initial metallization. This interplay between thermodynamics and kinetics governs phase evolution: thermodynamics favors a self-limiting amorphous Fe overlayer, while rapid oxide reduction kinetics drives amorphous overgrowth. Our findings demonstrate that amorphous intermediates bypass rate-limiting crystalline steps, providing mechanistic insights to optimize H 2 -based processes for sustainable steelmaking. In conclusion, these insights bridge the gap between macroscopic process engineering and atomic-scale dynamics, with broader implications for catalysis and nanostructured material synthesis, where oxide reduction pathways critically shape functional phases and microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Elucidating texture and grain morphology contributions to the micromechanical response of additively manufactured Inconel 625

Microstructural variation of additively manufactured (AM) metal components in comparison to wrought counterparts makes certification for critical applications a challenge. Microscale simulations leveraging modern computational tools may be used to supplement testing of AM microstructures, thus accelerating certification by reducing the number of experiments needed. However, as micromechanical response is closely tied to critical properties like fatigue-life and fracture, utilization of these simulations with macroscale experimental data alone is insufficient. One means to attain microscale experimental data is in situ diffraction data collected from synchrotron X-ray sources. In this work, such data were collected during in situ compression of AM Inconel 625 superalloy. Interpretation of experimental results was assisted by massive (8M element) complementary micromechanical simulations performed on sets of virtual microstructures generated using cellular automata. Together, micromechanical data from diffraction experiments and simulations were used to probe the effects of textured “track” microstructures generated during laser powder bed fusion and directional strength-to-stiffness on micromechanical response. Though fiber-averaged directional strength-to-stiffness ratios were expected to dominate given the high elastic anisotropy of the material, the combination of small variations in texture and specific grain configurations unique to AM microstructures lead to significant variability in micromechanical response after yield. The findings emphasize the importance of high-fidelity microstructural representation that captures key texture components and AM-specific morphology for property prediction of AM metals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structural and magnetic properties of hard magnetic system Ce(Co 1 -Fe ) 4.4 Cu 0.6 (0 ≤ x ≤ 0.19)

The Ce(Co 1 -Fe ) 4.4 Cu 0.6 (0 ≤ x ≤ 0.19) is a composite, hard magnetic system that is based on the CaCu 5 -type structure (1:5). It shows both, unique magnetic and microstructural features that are essential for permanent magnets, e.g., exceptional squareness of the 2nd. quadrant of the magnetization loops and microstructural features typically needed for pinning. Samples solidified in alumina crucibles are coarse-grained and often clearly faceted and readily align in a magnetic field. X-ray, SEM, and TEM analyses show a 1:5-type single-phase material when quenched from high temperature, which, after heat treatment, transforms into a laminar coherent nanostructure through the formation of a dense array of extended intercalated regions. Furthermore, these extended intercalated regions are comprised of segments of the Ce 2 Ni 7 –type structure (2:7) which segregate into various closely related precipitates forming a nanostructure similar to the SmCo 5 - Sm 2 Co 17 composites seen in Sm-Co permanent magnets. Based on TEM and Lorentz microscopy of well-aligned single grain particles, the magnetic domains’ reversal mechanism is regulated by anisotropy fluctuations occurring along the easy direction of magnetization and strong exchange interactions between the matrix and defects (e.g.: stacking faults). Lorentz microscopy suggests the domain wall is not physically pinned by the defect, but rather is offset/deflected when it interacts with the defect. The Lorentz and magnetization data suggest that defects cause a bending of the moment away from the c axis inside the grains.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Prediction of Probabilistic Shock Initiation Thresholds of Energetic Materials Through Evolution of Thermal-Mechanical Dissipation and Reactive Heating

The ignition threshold of an energetic material (EM) quantifies the macroscopic conditions for the onset of self-sustaining chemical reactions. The threshold is an important theoretical and practical measure of material attributes that relate to safety and reliability. Historically, the thresholds are measured experimentally. In this work, we present a new Lagrangian computational framework for establishing the probabilistic ignition thresholds of heterogeneous EM out of the evolutions of coupled mechanical-thermal-chemical processes using mesoscale simulations. Furthermore, the simulations explicitly account for microstructural heterogeneities, constituent properties, and interfacial processes and capture processes responsible for the development of material damage and the formation of hotspots in which chemical reactions initiate. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, heat conduction, reactive chemical heating, gaseous product generation, and convective heat transfer. To determine the ignition threshold, the minimum macroscopic loading required to achieve self-sustaining chemical reactions with a rate of reactive heat generation exceeding the rate of heat loss due to conduction and other dissipative mechanisms is determined. Probabilistic quantification of the processes and the thresholds are obtained via the use of statistically equivalent microstructure sample sets (SEMSS). The predictions are in agreement with available experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ultra-High Temperature Thermal Barrier Coating Development and Validation

The most effective method to improve industrial gas turbine power is to increase firing temperature. To maintain component durability at these higher temperatures, thermal barrier coatings (TBC’s) are playing an increasing role in protecting turbine hardware. TBC’s have been successfully used on Solar Turbines’ combustor liners and turbine airfoils, but there is a need for TBC’s that can operate at higher temperatures. Based on recent, favorable rig testing experience, Solar recognized that yttrium aluminum garnet (YAG) TBC’s made by the Solution Precursor Plasma Spray Process (SPPS) has great potential for higher turbine temperatures. This testing verified the thermal cycle durability of the SPPS YAG TBC’s, as well as demonstrated that the temperature drop through the SPPS YAG TBC was twice that measured across a companion APS YSZ TBC. The SPPS YAG TBC with the potential for a 200°C improvement in temperature capability represents a materials breakthrough. The SPPS process also produces a number of unique microstructural features that confer superior properties. SPPC TBC’s have ultra-fine splats that increase toughness and erosion resistance, and the process can be tailored to produce through-thickness cracks for strain-tolerance and control porosity for lower thermal conductivity and increase coating abradability. This proposal would include further development of SPPS YAG density-graded coatings, optimization of the application process, and validation of higher temperature capability and durability through rig and engine testing of coated combustion liners and turbine outer air seals. The technology would be advanced from a TRL 3 to a proposed TRL 6 by overcoming processing concerns on efficiency, application distance and optimization of a graded coating without impacting coating durability by demonstrating the coating performance on components in rig and development gas turbine engine testing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Low-Cost, Efficient and Durable High Temperature Wireless Sensors by Direct Write Additive Manufacturing for Application in Fossil Energy Systems

In-situ monitoring of physical parameters in fossil energy (FE) systems is challenging because of their high operating temperatures and oxidizing environments. This research aims utilize Aerosol Jet (AJ) nanoparticle printing, an advanced additive manufacturing method, to fabricate high temperature sensors for FE applications. Sensor films were fabricated by the AJ printing method with an excellent control over film microstructure and properties, in addition to creating less waste. Printing of six different materials, namely, silver (Ag), nickel (Ni), nickel-chromium (NiCr) alloy, copper (Cu), copper-nickel (CuNi), and carbon nanotubes (CNTs) was carried out for their evaluation of as sensor films. The films were then characterized using impedance spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS). Film microstructure was correlated with the sensor electrical and mechanical behavior at different temperatures (24-500 °C) and frequencies (0.02 - 300 kHz). This knowledge was then used to fabricate, test, and characterize strain and temperature sensors for high temperature applications. The strain sensors were shown to have a very low thermal strain up to 500 °C and a 57% higher gauge factor compared to the commercially available counterparts. This enhance performance was attributed to the inherent porosity in the sensor films that allowed the films to exhibit a metamaterial-like behavior when deformed. The AJ printed temperature sensors were shown to have the highest Seebeck coefficient amongst all the film-based temperature sensors. In addition, fundamental aspects of crystal structure and phase transformations in Ba(Fe0.7Ta0.3)O3-δ (BFTO), a high temperature sensor material, was investigated along with 3D printed antennas that can transmit sensor signals at high temperatures. Two PhD students, one from first generation college-going underrepresented minority community (URM), two postdoctoral fellows, and several masters and undergraduate students were supported by this award. This work opens up the possibility of realizing low-cost high-performance sensor devices using advanced manufacturing techniques for high temperature FE applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

He bubble-induced phase transformation of W grain boundaries revealed by accelerated molecular dynamics

The growth of He bubbles and the resulting impact on the microstructural evolution of W are of paramount importance to the plasma-facing materials community due to the application of W in Tokamak fusion reactors. Using accelerated molecular dynamics (AMD) techniques, we outline the structural evolution of grain boundaries (GBs) caused by growing He bubbles. It is discovered that when an alternative, low energy, high density GB structure or phase is available, He bubbles can induce a progressive phase transformation of the GB to the higher density phase by the continual nucleation of W Frenkel pairs. Here, we find that the resulting W self-interstitials migrate to sites at the GB which are structurally related to the higher density phase, leading to the transformation. We discuss the implications of this progressive microstructural evolution on the growing He bubble and consider in general how He bubbles will impact the structural evolution of an arbitrary W GB. These findings of GB phase transformation are predicted to impact other damage events in W such as recrystallization, GB migration and defect segregation which must take these findings into account in order to accurately simulate a realistic W microstructure and hence extract experimentally meaningful data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tuning working potential of silicon-phosphorus anode via microstructure control for high-energy lithium-ion batteries

The primary research on anode materials of lithium-ion batteries have been focused on increasing the specific capacity, while the working potential that is also closely related to the practical energy density of batteries has been paid much less attention. In this paper, starting from micrometer-sized silicon and black phosphorus, we have reported a high-energy silicon-phosphorus/carbon anode (denoted as mSPC) via a high-energy ball milling process, which demonstrates an average discharge working potential of 0.3 volts versus lithium, together with a high reversible capacity of > 2000 mAh/g, high initial coulombic efficiency of 84%, excellent cycle stability, and superior rate capability up to 15 A/g. Furthermore, in situ focused-ion-beam scanning electron microscopy reveals that the volume change of the mSPC anode during repeated (de) lithiation is effectively alleviated. In contrast, starting from nanometer-sized silicon, the resulted anode (denoted as nSPC) not only presents a lower reversible capacity (~1200 mAh/g), but also exhibits a higher charge/discharge working potential, leading to reduced energy density. Our results indicate the importance of composition/structure control in tailoring the working potential and specific capacity of alloying-type anodes towards high-energy lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Influence of geometry on columnar to equiaxed transition during electron beam powder bed fusion of IN718

Correlation between spot-melt scan parameters (linear spot-density aka areal energy density), build geometry, and solidification microstructure evolution (columnar vs equiaxed) in a powder bed fusion technology is investigated. It is shown that to maintain the equiaxed solidification microstructure evolution in electron beam powder bed additive manufacturing (AM), the areal energy density per layer needs to be scaled with respect to the 2D cross-sectional area of the layer being melted. Samples with two different cross-sectional areas (40 × 40 mm and 20 × 20 mm) have been fabricated with varying areal energy densities. For a given square cross-section (20 × 20 mm), increasing the areal energy density (4.8 MJ/sq.m to 14.7 MJ/sq.m) transitioned the solidification microstructure from columnar to equiaxed. The observed microstructure data (Electron Back Scattered Diffraction - EBSD) is quantified by calculating the principal component (PC) score using a spatial statistics methodology. The sample with equiaxed grains is found to have a low PC score while the sample with columnar grain had a high PC score. A semi-analytical model is used to simulate the heat transfer and the local solidification conditions as a function of processing parameters (linear spot-density). The result from the heat transfer model is correlated with previously quantified microstructure data. Space-Time analysis of the melt pattern is done and correlated with the observed microstructure. In addition, from the findings, appropriate parameters have been used to additively manufacture a turbine blade with site-specific or hybrid solidification microstructure (traditional fabrication possible via a patented method of localized cold working and heat treatment).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Characterizing Copper-Graphene Composites (Abstract)

In this project, Pacific Northwest National Laboratory (PNNL) will measure the electrical and mechanical properties of the metal composite samples provided by MetalKraft Technologies, LLC. PNNL will also use multimodal methods for characterizing the microstructure of MetalKraft Technologies, LLC’s copper graphene composites. PNNL will work with MetalKraft Technologies, LLC as part of the CABLE Manufacturing Prize efforts under a CRADA. The voucher from DOE has a budget of $100,000 over a period of 6 months. PNNL will be the primary place of performance for the property and microstructure characterization efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response

A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. Furthermore, an iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Phase-field model of alloy solidification far from chemical equilibrium at the solid-liquid interface

We further develop a recently introduced phase-field model of far-from-equilibrium alloy solidification under additive manufacturing conditions [K. Ji et al., Phys. Rev. Lett. 130, 026203 (2023)]. This model utilizes enhanced solute diffusivity within the spatially diffuse interface region to quantitatively capture solute trapping with a larger interface width, thereby making simulations on experimentally relevant length and timescales computationally feasible. The main developments presented here include testing the robustness of different variational formulations, extending the model to concentrated alloys by incorporating solid and liquid free energies from thermodynamic databases, as illustrated for hypoeutectic Al-Ag alloys with CALPHAD, extending convergence tests as a function of interface width to 3D, and carrying out simulations in both 2D and 3D to examine existing theories of microstructure development. Our results indicate that the simplest variational formulation that interpolates the bulk free-energy density between its solid and liquid forms is the most robust. Remarkably, for hypoeutectic Al-Ag alloys, this formulation yields a high-velocity nonequilibrium phase diagram that is independent of interface width, thereby demonstrating that the framework of enhanced solute diffusivity can be nontrivially extended to concentrated alloys. Other variational formulations have restricted ranges of materials or processing parameters that can be reliably modeled. We use 2D simulations to construct high-velocity microstructure selection maps for dilute Al-Cu alloys. The results validate the important role of latent heat rejection at the interface and extend the limited predictions of linear stability analysis [A. Karma and A. Sarkissian, Phys. Rev. E 47, 513 (1993)] and sharp-interface 1D simulations to fully nonlinear regimes. Furthermore, 3D simulations, carried out using a computationally tractable axisymmetric cellular/dendritic interface shape, demonstrate a good convergence similar to that observed in 2D as a function of interface width. Full 3D simulations, in turn, reveal that the standard theory of absolute stability is a good predictor of the upper critical velocity beyond which steady-state growth becomes unstable, despite the different morphological manifestations of this instability in 2D and 3D.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗