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At least 1,441 records · Page 80

Quantitative analysis of inclusion distributions in hot pressed silicon carbide

ABSTRACT Depth of penetration measurements in hot pressed SiC have exhibited significant variability that may be influenced by microstructural defects. To obtain a better understanding regarding the role of microstructural defects under highly dynamic conditions; fragments of hot pressed SiC plates subjected to impact tests were examined. Two types of inclusion defects were identified, carbonaceous and an aluminum-iron-oxide phase. A disproportionate number of large inclusions were found on the rubble, indicating that the inclusion defects were a part of the fragmentation process. Distribution functions were plotted to compare the inclusion populations. Fragments from the superior performing sample had an inclusion population consisting of more numerous but smaller inclusions. One possible explanation for this result is that the superior sample withstood a greater stress before failure, causing a greater number of smaller inclusions to participate in fragmentation than in the weaker sample.

36 MATERIALS SCIENCE↗

Scanning Electron Microscopy Analysis of Fuel/Matrix Interaction Layers in Highly-Irradiated U–Mo Dispersion Fuel Plates with Al and Al–Si Alloy Matrices

In order to investigate how the microstructure of fuel/matrix-interaction (FMI) layers change during irradiation, different U–7Mo dispersion fuel plates have been irradiated to high fission density and then characterized using scanning electron microscopy (SEM). Specifially, samples from irradiated U–7Mo dispersion fuel elements with pure Al, Al–2Si and AA4043 (~4.5 wt.%Si) matrices were SEM characterized using polished samples and samples that were prepared with a focused ion beam (FIB). Features not observable for the polished samples could be captured in SEM images taken of the FIB samples. For the Al matrix sample, a relatively large FMI layer develops, with enrichment of Xe at the FMI layer/Al matrix interface and evidence of debonding. Overall, a significant penetration of Si from the FMI layer into the U–7Mo fuel was observed for samples with Si in the Al matrix, which resulted in a change of the size (larger) and shape (round) of the fission-gas bubbles. Additionally, solid-fission-product phases were observed to nucleate and grow within these bubbles. These changes in the localized regions of the microstructure of the U–7Mo may contribute to changes observed in the macroscopic swelling of fuel plates with Al–Si matrices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Electrochemical Behavior Assessment of As-Cast Mg-Y-RE-Zr Alloy in Phosphate Buffer Solutions (X Na{sub 3}PO{sub 4} + Y Na{sub 2}HPO{sub 4}) Using Electrochemical Impedance Spectroscopy and Mott–Schottky Techniques

In the present study, electrochemical behavior of as-cast Mg-Y-RE-Zr alloy (RE: rare-earth alloying elements) was investigated using electrochemical tests in phosphate buffer solutions (X Na{sub 3}PO{sub 4} + Y Na{sub 2}HPO{sub 4}). X-ray diffraction techniques and Scanning electron microscopy equipped with energy dispersive x-ray spectroscopy were used to investigate the microstructure and phases of the experimental alloy. Different electrochemical tests such as potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS) and Mott–Schottky (M–S) analysis were carried out in order to study the electrochemical behavior of the experimental alloy in phosphate buffer solutions. The PDP curves and EIS measurements indicated that the passive behavior of the as-cast Mg-Y-RE-Zr alloy in phosphate buffer solutions was weakened by an increase in the pH, which is related to formation of an imperfect and less protective passive layer on the alloy surface. The presence of the insoluble zirconium particles along with high number of intermetallic phases of RE elements mainly Mg{sub 24}Y{sub 5} in the magnesium matrix can deteriorate the corrosion performance of the alloy by disrupting the protective passive layer that is formed at pH values over 11. These insoluble zirconium particles embedded in the matrix can detrimentally influence the passivation. The M–S analysis revealed that the formed passive layers on Mg-Y-RE-Zr alloy behaved as an n-type semiconductor. An increase in donor concentration accompanying solutions of higher alkalinity is thought to result in the formation of a less resistive passive layer.

36 MATERIALS SCIENCE↗

Phase Evolution and Mechanical Properties of AlCoCrFeNiSi{sub x} High-Entropy Alloys Synthesized by Mechanical Alloying and Spark Plasma Sintering

In the current investigation, AlCoCrFeNiSi{sub x} (x = 0, 0.3, 0.6 and 0.9 in atomic ratio) high-entropy alloy systems are prepared by mechanical alloying and subsequently consolidated by spark plasma sintering. The microstructural and mechanical properties were analyzed to understand the effect of Si addition in AlCoCrFeNi alloy. The x-ray diffraction analysis reveals the supersaturated solid solution of the body-centered cubic structure after 20 h of ball milling. However, the consolidation promotes the transformation of body-centered phases partially into the face-centered cubic structure and sigma phases. A recently proposed geometric model based on the atomic stress theory has been extended for the first time to classify single phase and multi-phases on the high-entropy alloys prepared by mechanical alloying and spark plasma sintering process. Improved microhardness and better wear resistance were achieved as the Si content increased from 0 to 0.9 in the present high-entropy alloy.

36 MATERIALS SCIENCE↗

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. Here, the variations in the resulting analyses are explored due to the influence of new and more varied data.

36 MATERIALS SCIENCE↗

Characterizing the microstructures of mammalian enamel by synchrotron phase contrast microCT

The enamel of mammalian teeth is a highly mineralized tissue that must endure a lifetime of cyclic contact and is inspiring the development of next-generation engineering materials. Attempts to implement enamel-inspired structures in synthetic materials have had limited success, largely due to the absence of a detailed understanding of its microstructure. Here, the present work used synchrotron phase-contrast microCT imaging to evaluate the three-dimensional microstructure of enamel from four mammals including Lion, Gray Wolf, Snow Leopard, and Black Bear. Quantitative results of image analysis revealed that the decussation pattern of enamel consists of discrete diazone (D) and parazone (P) bands of rods organized with stacking arrangement of D+/P/D-/P in all mammals evaluated; the D+ and D- refer to distinct diazone bands with juxtaposed rod orientations from the reference plane. Furthermore, the rod orientations in the bands can be described in terms of two principal angles, defined here as the pitch and yaw. While the pitch angle increases from the outer enamel to a maximum (up to ≈ 40°) near the dentin enamel junction, minimal spatial variations are observed in yaw across the enamel thickness. There are clear differences in the decussation parameters of enamel across species that are interpreted here with respect to the structural demands placed on their teeth. The rod pitch and band width of enamel are identified as important design parameters and appear to be correlated with the bite force quotient of the four mammals evaluated.

36 MATERIALS SCIENCE↗

Approaching the Practical Conductivity Limits of Aerosol Jet Printed Silver

Previous efforts to directly write conductive metals have been narrowly focused on nanoparticle ink suspensions that require aggressive sintering (>200°C) and result in low-density, small-grained agglomerates with electrical conductivities <25% of bulk metal. Here, we demonstrate aerosol jet printing of a reactive ink solution and characterize high-density (93%) printed silver traces having near-bulk conductivity and grain sizes greater than the electron mean free path, while only requiring low temperature (80°C) treatment. We have developed a predictive electronic transport model which correlates the microstructure to the measured conductivity and identifies a strategy to approach the practical conductivity limit for printed metals. Our analysis of how grain boundaries and tortuosity contribute to electrical resistivity provides insight into the basic materials science that governs how an ink formulator or process developer might approach improving the conductivity. Transmission line measurements validate that electrical properties are preserved up to 20GHz, which demonstrates the utility of this technique for printed RF components. Finally, this work reveals a new method of producing robust printed electronics that retain the advantages of rapid prototyping and three-dimensional fabrication while achieving the performance necessary for success within the aerospace and communications industries.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Coupling physics in machine learning to predict properties of high-temperatures alloys

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

36 MATERIALS SCIENCE↗

Bridging Nano- and Microscale X-Ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

diagnostics↗

Multiscale analysis in solids with unseparated scales: fine-scale recovery, error estimation, and coarse-scale adaptivity

There are several engineering applications in which the assumptions of homogenization and scale separation may be violated, in particular, for metallic structures constructed through additive manufacturing. Instead of resorting to direct numerical simulation of the macroscale system with an embedded fine scale, an alternative approach is to use an approximate macroscale constitutive model, but then estimate the model-form error using a posteriori error estimation techniques and subsequently adapt the macroscale model to reduce the error for a given boundary value problem and quantity of interest. Here, we investigate this approach to multiscale analysis in solids with unseparated scales using the example of an additively manufactured metallic structure consisting of a polycrystalline microstructure that is neither periodic nor statistically homogeneous. As a first step to the general nonlinear case, we focus here on linear elasticity in which each grain within the polycrystal is linear elastic but anisotropic.

42 ENGINEERING↗

Summary of BISON Development and Validation Activities - NEAMS FY15 Report

The objective of the NEAMS ToolKit is to develop a “pellet-to-plant” simulation capability useful for predicting performance and safety for a broad range of nuclear reactor power systems. The NEAMS ToolKit has been organized into a Fuels Product Line (FPL) and a Reactor Product Line (RPL) and is modular in design. Within the FPL, a multiscale approach has been adopted in which simulations of fuel performance at the engineering scale are informed by material property and irradiation performance models developed from mesoscale simulations of microstructural evolution. The focus in this report is on development and validation of the engineering-scale fuel performance analysis tool within the FPL, which is BISON. This summary report contains an overview of work performed under the work package enti- tled “FY2015 NEAMS INL-Engineering Scale Fuel Performance & Interface with RPL Tools.” A first chapter identifies the specific FY-15 milestones, providing a basic description of the asso- ciated work and references to related detailed documentation. Where applicable, a representative technical result is provided. A second chapter summarizes major additional accomplishments including 1) a BISON validation paper, 2) a multiphysics coupling paper, 3) application of BI- SON to Reactivity Insertion Accident analysis and 4) use of BISON for uncertainity analysis in fuel modeling. A final chapter outlines FY-16 future work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Characterization of Radiation Induced Defects Across Scales

The Whole Powder Pattern Modelling (WPPM) approach of diffraction line profile analysis (LPA) was applied for the first time on PuO 2 in order to understand the evolution of the microstructural properties as a function of process conditions. Crystallite size distributions and defect concentrations were determined by WPPM on three different PuO 2 samples - including one with a bimodal distribution and compared to the result of more common LPA analyses used in PuO 2 literature studies (e.g. Scherrer equation, double Voigt method). The conclusion was that a lognormal distribution of spherical crystallites with dislocations most closely represent the microstructure of PuO 2 produced by heating Pu 2 (C 2 O 4 ) 3 (H 2 O) 6 •3H 2 O between 650°C and 850°C. The volume-weighted mean column length (Lvol) parameter obtained from the double Voigt method for the monomodal samples were the same as the Lvol obtained from the WPPM within uncertainty. The two LPA methods did not agree on the Lvol parameters of the bimodal distribution sample with only the WPPM getting values that were consistent with the individual components. The WPPM can potentially provide more insight into future studies of the influences on the microstructure properties of PuO 2 .

36 MATERIALS SCIENCE↗

MAGNESIUM PRECIPITATION AND DIFUSSION IN Mg+ ION IMPLANTED SILICON CARBIDE

As a candidate material for fusion reactor applications, silicon carbide (SiC) undergoes transmutation reactions under high-energy neutron irradiation with magnesium as the major metallic transmutant; the others include aluminum, beryllium and phosphorus in addition to helium and hydrogen gaseous species. Calculations by Sawan et al. predict that at a dose of ~100 dpa (displacements per atom), there is ~0.5 at.% Mg generated in SiC. The impact of these transmutants on SiC structural stability is currently unknown. This study uses ion implantation to introduce Mg into SiC. Multiaxial ion-channeling analysis of the as-produced damage state indicates a lower dechanneling yield observed along the <100> axis. The microstructure of the annealed sample was examined using high-resolution scanning transmission electron microscopy. The results show a high concentration of likely non-faulted tetrahedral voids and possible stacking fault tetrahedra near the damage peak. In addition to lattice distortion, dislocations and intrinsic and extrinsic stacking faults are also observed. Magnesium in 3C–SiC prefers to substitute for Si and it forms precipitates of cubic Mg2Si and tetragonal MgC2. The diffusion coefficient of Mg in 3C–SiC single crystal at 1573 K has been determined to be 3.8 ± 0.4E-19 m2/s.

diffusion↗

Affine Transformations to Correlate Experimental and Simulated EDS Spectra for Multi-Element Systems

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Detection of secondary phases in duplex stainless steel by magnetic force microscopy and scanning Kelvin probe force microscopy

The secondary phase transformations in a commercial super duplex stainless steel were investigated by micro-chemical analyses and high resolution scanning probe microscopy. Energy dispersive X-ray and electron probe detected ferrite and austenite as well as secondary phases in unetched aged duplex stainless steel type 25Cr-7Ni-3Mo. Volta potential indicated that nitride and sigma appeared more active than ferrite, while secondary austenite and austenite presented a nobler potential. Reversal order in nobility is thought to be attributable to the potential ranking provided by oxide nature diversity as a result of secondary phase surface compositions on steel. After eutectoid transformation, secondary austenite was detected by electron probe microanalysis, whereas atomic force microscopy distinguished this phase from former austenite by image contrast. Magnetic force microscopy revealed a “ghosted” effect on the latter microstructure probably derived from metal memory reminiscence of mechanical polishing at passivity and long range magnetic forces of ferrite phase. - Highlights: • Nobility detection of secondary phases by SKPFM in DSS particles is not a straightforward procedure. • As Volta potential and contrast are not always consistent SKPFM surface oxides is thought played an important role in detection. • AFM distinguished secondary austenite from former austenite by image contrast though SEM required EPMA.

36 MATERIALS SCIENCE↗

Glass structure and crystallization in boro-alumino-silicate glasses containing rare earth and transition metal cations: a US-UK collaborative program

Nuclear wastes generated from reprocessing of used nuclear fuel tend to contain a large fraction of rare earth (RE, e.g., Nd3+), transition (TM, e.g., Mo6+, Zr4+), alkali (A, e.g., Cs+), and alkaline earth cations (AE, e.g., Ba2+, Sr2+). Various strategies have been considered for immobilizing such waste streams, varying from nominally crystal-free glass to glass-ceramic to multi-phase ceramic waste forms. For glass and glass-ceramic waste forms, the added glass-forming system is generally alkali-alkaline earth-aluminoborosilicate (i.e., Na-Ca-Al-B-Si oxide). In a US-UK collaborative project, summarized here, we investigated the glass structure and crystallization dependence on compositional changes in simulated nuclear waste glasses and glass-ceramics. Compositions ranged in complexity from five – to – eight oxides. Specifically, the roles of Mo and rare earths are investigated, since a proposed glass-ceramic waste form contains crystalline phases such as powellite [(AE,A,RE)MoO4] and oxyapatite [(RE,AE,A)10Si6O26], and the precipitation of molybdenum phases is known to be affected by the rare earth concentration in the glass. Additionally, the effects of other chemical additions have been systematically investigated, including Zr, Ru, P, and Ti. A series of studies were also undertaken to ascertain the effect of the RE size on glass structure and on partitioning to crystal phases, investigating similarities and differences in glasses containing single RE oxides of Sc, Y, La, Ce, Nd, Sm, Er, Yb, or Lu. Finally, the effect of charge compensation was investigated by considering not only the commonly assessed peralkaline glass but also metaluminous and peraluminous compositions. Glass structure and crystallization studies were conducted by spectroscopic methods (i.e., Raman, X-ray absorption, nuclear magnetic resonance (NMR), optical absorption, photoluminescence, photoluminescence excitation, X-ray photoelectron spectroscopy), microscopy (i.e., scanning electron microscopy, transmission electron microscopy, electron probe microanalysis), scattering (i.e., X-ray and neutron diffraction, small angle measurements), and physical characterization (i.e., differential thermal analysis, liquidus, viscosity, density). This paper will give an overview of the research program and some example unpublished results on glass-ceramic crystallization kinetics, microstructure, and Raman spectra, as well as some examples of the effects of rare earths on the absorption, luminescence, and NMR spectra of starting glasses. The formal collaboration described here has resulted in the generation of a large number of results, some of which are still in the process of being published as separate studies.

McCloy, John S.↗

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗

Slip localization behavior at triple junctions in nickel-base superalloys

Incipient slip localization in the vicinity of hundreds of grain boundary triple junctions (TJs) in a lightly deformed nickel-base superalloy IN718 is studied using a combination of three-dimensional (3D) crystal plasticity finite element (CPFE) modeling, high resolution digital image correlation (HR-DIC) and 3D electron back-scatter diffraction tomography (3D EBSD). A 3D reconstruction method enables identification of thousands of TJs and correspondence of any observed slip bands with their originating TJ lines below the specimen surface. Here, we present a large-scale CPFE model of the experimental 3D microstructure composed of high-fidelity representation of the TJ lines and the boundaries and interiors of the parent grains and use it to calculate the local micromechanical response and slip activity of all TJs at the onset of macroscopic yielding. Statistical analysis of the calculated quantities reveal TJs develop larger stress concentration and grain-average re-orientation than grain interiors and grain boundaries, however no substantial differences in cumulative slip were found among these microstructural regions. We find that TJs with observed slip bands generate lower grain-average re-orientation, fewer active slip systems, and more localized slip on a single system than those without. The distinctions in the reorientation and slip activity are stronger in TJs that experience more intense slip.

36 MATERIALS SCIENCE↗