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Transmission electron microscopy and optical microscopy were used to examine polycrystalline specimens of the B2-structured alloy FeAl strained under tension to fracture at elevated temperature. Strain-induced grain boundary migration was observed above 900 K and dynamic recrystallization was found at 1000 K and 1100 K. Little evidence of dynamic recovery was evident but some networks were formed at 1100 K.
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Understanding the impact of microstructure on corrosion rates can aid the development of corrosion-resistant alloys for molten salt reactors. Here in this work, we develop an electrochemical phase-field model for capturing the microstructure-dependent corrosion of structural alloys by molten salts. As a demonstration problem, we apply this model to capture the selective depletion of Cr from Ni-Cr grain boundaries during corrosion in molten FLiBe salt. We perform sensitivity analysis and model verification on 1D simulations to confirm that the model predicts diffusion-limited kinetics. The model is validated using 1D, 2D, and 3D simulations against experimental data for Ni-5Cr and Ni-20Cr corrosion in molten FLiBe. The 1D simulations predict the corrosion behavior with reasonable accuracy when using an effective diffusion coefficient that accurately represents the grain boundary diffusion. 2D simulations that represent the grain structure underpredict the corrosion. 3D simulations that represent the grain structure predict the corrosion with reasonable accuracy. The corrosion rate predicted by the 3D simulations is proportional to the average grain size at the alloy/salt interface.
Here, we investigate the heterogeneity of the stress state driven by anisotropic deformation response at the single crystal level through five statistical volume element (SVE) calculations of polycrystalline BCC tantalum. This work focuses on grain boundaries as a prominent material defect type prone to void nucleation based upon experimental observations of predominantly intergranular void nucleation in this material. The SVEs are constructed to be statistically representative of larger volumes of material and are meshed such that mean and standard deviation of grain size and orientation information is reconstructed. The computational meshes feature hexahedral (brick) elements and smooth conformal grain boundaries where significant stress concentration is known to occur, a tail effect of interest in the extreme events process of dynamic ductile damage. An existing micromechanical crystallographic plasticity model shown to capture the single crystal behavior of BCC tantalum well is used to perform the polycrystal calculations. The model includes representation of the non-Schmid effect of non-planar screw dislocation kinetics in tantalum. A three-dimensional stress state time profile predicted by damage modeling of a flyer plate impact experiment is applied as boundary conditions to each SVE. Resulting grain boundary stress state statistics are strongly non-Gaussian. Significant structural evolution is observed within the compressive hold before unloading into tension in the stress profile. Strong angular dependence of grain boundary traction magnitude with shock direction is observed. Non-Schmid effects continue to suggest their influence on propensity of microstructural defect types to nucleate voids. A general void nucleation criterion is proposed using probability theory. The general framework is specified to polycrystalline BCC tantalum in the weak shock regime to include the SVE calculations and literature molecular dynamics calculations of grain boundary void nucleation strength. Probability density functions (PDFs) are used to describe the interaction between the local stress state heterogeneity and the distributed grain boundary void nucleation strength state. A causation entropy maximization procedure removes the requirement for ad hoc selection of a PDF functional form and provides a rigorous procedure for data-based PDF determination. The resulting physically informed PDF describes the spatial appearance frequency of nucleated voids as a function of applied macroscale pressure. Lower length scale physics are thus packaged in a precise and computationally efficient way to provide computational plasticity insight to macroscale dynamic ductile damage models.
One hundred ninety-three silicon sheet samples, approximately 880 square centimeters, were analyzed for twin boundary density, dislocation pit density, and grain boundary length. One hundred fifteen of these samples were manufactured by a heat exchanger method, thirty-eight by edge defined film fed growth, twenty-three by the silicon on ceramics process, and ten by the dendritic web process. Seven solar cells were also step-etched to determine the internal defect distribution on these samples. Procedures were developed or the quantitative characterization of structural defects such as dislocation pits, precipitates, twin & grain boundaries using a QTM 720 quantitative image analyzing system interfaced with a PDP 11/03 mini computer. Characterization of the grain boundary length per unit area for polycrystalline samples was done by using the intercept method on an Olympus HBM Microscope.
Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ3(111)[110] and Σ3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ3(111)[110] Γ=1, ±0.5 and Σ3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.
Iron-based superconductors (IBSCs) are a class of material under investigation for the development of superconducting wires in the low-temperature-high magnetic fields power application. Among the various families of IBSCs, the 1144 CaKFe$_4$As$_4$ compound is a promising material able to achieve outstanding superconducting properties with a cheap and simple chemical composition. Oxidation, in these compounds, is considered an obstacle for high intergranular critical current density, J c,GB . A study devoted to the evaluation of oxidation phenomena and their effects on the superconducting properties is thus needed in order to fully understand the involved mechanisms. From the evaluation of polycrystalline samples obtained by a mechanochemically assisted synthesis route, a degradation of the critical temperature and critical currents has been observed concurrently with oxygen accumulation at grain boundaries in open porosities. However, the crystalline structure at an atomic level seems not affected, as well as intragranular superconducting properties assessed by means of calorimetric methods. These results suggest that loss of superconducting properties in Ca/K-1144 compounds following oxidation is significantly associated with the worsening of grain connectivity.
The segregation of solute atoms at grain boundaries (GBs) can strongly impact the structural and functional properties of polycrystals. Yet, due to the limited availability of simulation tools to study polycrystals at the atomistic scale (i.e., interatomic potentials), there is a minimal understanding of the variation of solute segregation tendencies across the very complex space of GB microenvironments and the large range of alloys in which it can occur. Here, we develop an algorithmic framework that can directly learn the full spectrum of segregation energies for a metal solute atom in a metal polycrystal from ab initio methods, bypassing the need for alloy interatomic potentials. This framework offers a pathway to a comprehensive catalog of GB solute segregation with quantum accuracy, for the entire alloy space. As an initial demonstration in this pursuit, we build an extensive GB segregation database for aluminum-based alloys across the periodic table, including dozens of alloys for which there are substantially no prior data.
Grain boundary-based mechanisms are known to control the plastic deformation and failure of nanocrystalline metals, with manipulation of the boundary structure a promising path for tuning this response. In this study, the role of interfacial structural disorder on plasticity and failure of nanocrystalline Cu-Zr alloys is investigated with in situ scanning electron microscopy tensile deformation experiments. Two model materials are created, one with only the typical ordered grain boundaries and another with amorphous intergranular films interspersed into the boundary network, while the microstructures are otherwise identical. Hence, the importance of complexion type on plasticity and failure is isolated by only varying complexion structure. Here, the tensile experiments show that failure of the samples containing amorphous films is significantly retarded, as evidenced by an increase in the cross-sectional area reduction, a decrease in the occurrence of shear-dominated failure, a decrease in strain localization, and fracture surfaces with more elongated dimple features. As a whole, this study provides direct evidence that structural disorder at the grain boundaries can be beneficial for improving the ductility of nanocrystalline metals.
The theory of grain boundary (the interface between crystallites, GB) structure has a long history 1 and the concept of GBs undergoing phase transformations was proposed 50 years ago 2,3 . The underlying assumption was that multiple stable and metastable states exist for different GB orientations 4,5,6 . The terminology ‘complexion’ was recently proposed to distinguish between interfacial states that differ in any equilibrium thermodynamic property 7 . Different types of complexion and transitions between complexions have been characterized, mostly in binary or multicomponent systems 8,9,10,11,12,13,14,15,16,17,18,19 . Simulations have provided insight into the phase behaviour of interfaces and shown that GB transitions can occur in many material systems 20,21,22,23,24 . However, the direct experimental observation and transformation kinetics of GBs in an elemental metal have remained elusive. In this work we demonstrate atomic-scale GB phase coexistence and transformations at symmetric and asymmetric $[11\bar{1}]$ tilt GBs in elemental copper. Atomic-resolution imaging reveals the coexistence of two different structures at Σ19b GBs (where Σ19 is the density of coincident sites and b is a GB variant), in agreement with evolutionary GB structure search and clustering analysis 21,25,26 . We also use finite-temperature molecular dynamics simulations to explore the coexistence and transformation kinetics of these GB phases. Our results demonstrate how GB phases can be kinetically trapped, enabling atomic-scale room-temperature observations. Our work paves the way for atomic-scale in situ studies of metallic GB phase transformations, which were previously detected only indirectly 9,15,27,28,29 , through their influence on abnormal grain growth, non-Arrhenius-type diffusion or liquid metal embrittlement.
By Increasing the powder layer thickness we could increase material deposition rates and reduce build times during laser powder bed fusion (LPBF). However, increasing the powder layer thickness could also negatively affect the local melting and subsequent microstructure of the printed material. This study systematically investigated, for the first time, the defect evolution and microstructural changes of LPBF stainless steel 316 (SS316) processed by using various powder layer thicknesses of 40, 80, and 120 μm while also varying the laser dwell time for the pulsed laser. Through image analysis, the pore density was found to increase with increasing powder layer thickness, particularly when the dwell time was too high or too low. The maximum achievable density (>99%) was found over a wide processing window when using the smallest powder layer thickness of 40 μm between dwell times of 60 and 100 μs. Increasing the powder layer thicknesses to 80 and 120 μm resulted in maximum densities of 98.7 and 96.8%, respectively, but the window for acceptable laser dwell times that could achieve these densities narrowed considerably. A microstructural analysis of the melt pools was performed to measure the melt pool depths and widths, both of which increased with increasing dwell time. However, increasing the powder layer thickness did not affect the melt pool depth and only minorly affected the width. A sub-grain cellular structure distinguished the melt pool boundaries. The cell size increased with increasing dwell time and decreasing powder layer thickness. Moreover, the cell size was used to calculate a cooling rate that had a magnitude of 10 7 K/s and increased with increasing powder layer thickness.
Cu-based catalysts for the electrochemical reduction of CO 2 and CO exhibit a perplexingly unique reactivity toward multicarbon based products compared to other studied electrocatalysts. Here, in this study, we use insights gained from a recent phenomenological 3-site microkinetic model and grand-canonical density functional theory calculations to clarify the importance of an underemphasized aspect critical to Cu’s unique reactivity: a population of so-called “reservoir” sites. Using model Cu surface motifs, we discuss how these types can be represented by undercoordinated structural defects like step edges and grain boundaries which form a network of highly anisotropic migration channels. These pathways are found to be amenable for feeding *CO over time to reactive sites like Cu adatoms more active toward C–C coupling. These results highlight an often overlooked aspect of catalyst optimization: reservoir site engineering, which exploits surface mobility and presents an equally important avenue for electrocatalyst engineering oversimply maximizing active site densities.
The surfaces of textured polycrystalline N-type bismuth telluride and P-type antimony telluride materials were investigated using ex situ photoelectron emission microscopy (PEEM). PEEM enabled imaging of the work function for different oxidation times due to exposure to air across sample surfaces. The spatially averaged work function was also tracked as a function of air exposure time. N-type bismuth telluride showed an increase in the work function around grain boundaries relative to grain interiors during the early stages of air exposure-driven oxidation. At longer time exposure to air, the surface became homogenous after a ~5 nm-thick oxide formed. X-ray photoemission spectroscopy was used to correlate changes in PEEM imaging in real space and work function evolution to the progressive growth of an oxide layer. The observed work function contrast is consistent with the pinning of electronic surface states due to the defects at a grain boundary.
The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.
Porous materials with high specific surface area, high porosity, and high electrical conductivity are promising materials for functional applications, including catalysis, sensing, and energy storage. Molten salt dealloying was recently demonstrated in microwires as an alternative method to fabricate porous structures. The method takes advantage of the selective dissolution process introduced by impurities often observed in molten salt corrosion. This work further investigates molten salt dealloying in bulk Ni–20Cr alloy in both KCl–MgCl 2 and KCl–NaCl salts at 700 °C, using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction (XRD), as well as synchrotron X-ray nano-tomography. Micro-sized pores with irregular shapes and sizes ranging from sub-micron to several microns and ligaments formed during the process, while the molten salt dealloying was found to progress several microns into the bulk materials within 1–16 h, a relatively short reaction time, enhancing the practicality of using the method for synthesis. The ligament size increased from ~ 0.7 μm to ~ 1.3 μm in KCl–MgCl 2 from 1 to 16 h due to coarsening, while remaining ~ 0.4 μm in KCl–NaCl during 16 h of exposure. The XRD analysis shows that the corrosion occurred primarily near the surface of the bulk sample, and Cr 2 O 3 was identified as a corrosion product when the reaction was conducted in an air environment (controlled amount sealed in capillaries); thus surface oxides are likely to slow the morphological coarsening rate by hindering the surface diffusion in the dealloyed structure. 3D-connected pores and grain boundary corrosion were visualized by synchrotron X-ray nano-tomography. This study provides insights into the morphological and chemical evolution of molten salt dealloying in bulk materials, with a connection to molten salt corrosion concerns in the design of next-generation nuclear and solar energy power plants.
Research on active control of large space structures is discussed. Intrinsic damping in monolithic metallic structures is discussed. Thermal relaxation and grain boundary relaxation are discussed, as are properties of thermal damping.
In this work nanoscale features that are of importance in the stability of yttria-stabilized tetragonal zirconia (Y-TZP) are quantified through atom probe tomography. In-depth analysis of grain boundary chemistry revealed preferential segregation of lighter and smaller ions towards specific grain boundaries. The relationship between elemental segregation and the local atomistic structure is investigated at the sub-nanometer level to gain insights on nanoscale features associated with the tetragonal-monoclinic phase transformation in Y-TZP through grain boundary characterization. Furthermore, principal component analysis was implemented to reveal any potential biases from varied field evaporation across stabilizer-rich grain boundaries. The observed variations in ion density across different grains suggested a variation in field which was attributed to potential variations in grain crystal orientation. In order to reveal the subtle depletion of oxygen atoms within grain boundaries a new methodology to map oxygen vacancies is proposed, utilizing the relative neighborhood chemistry of individual yttrium atoms.
A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.