Phloem transport of arsenic species from flag leaf to grain during grain filling
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The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.
Corn grain and chopped stover were stored anaerobically at 37% to 50% (w.b.) aggregate moisture content to create a unique biomass feedstock. However, after storage, the two fractions must be separated to accommodate different conversion pathways. In this work, a modified cleaning system from a grain combine harvester was evaluated to investigate its effectiveness in separating the grain from the stover. While this system has been purposefully built for this task, there is no previous literature on separating grain at high material other than grain (MOG) to grain ratios. Using this system, material was separated into four fractions: Grain, Tailings, Heavy MOG, and Light MOG. Subsamples were collected, oven dried, and then hydrodynamically separated to quantify the grain content in the four fractions. Several different configurations of air velocities, mass flow rate, material MOG: grain ratios, and sieve types and openings were investigated. Grain capture was defined as the fraction of the total grain dry mass collected in the grain and tailings fractions. In five experiments, the grain capture effectiveness varied from 89.2% to 99.4% on a dry basis. The dry basis foreign matter (FM) in the clean grain was 0.6% to 10.8%. As a result, the use of a modified combine cleaning system has the potential to be one step in a system to fractionate corn grain from stover (i.e., MOG) in a biorefinery that uses both starch and cellulose as biomass feedstocks.
The thermal conductivity of UO 2 fuel needs to be high enough to dissipate the heat generated from the fission reaction. Since grain size affects thermal conductivity and grain size can evolve with irradiation, it is critical to understand in-reactor UO 2 grain growth. Most studies of grain growth in UO 2 are based on thermally driven processes at elevated temperatures. However, studies have shown that grain growth can occur even at cryogenic temperatures by ballistic processes. Such irradiation-induced grain growth in UO 2 is yet to be studied. Advanced in-situ Kr ion irradiation and transmission electron microscopy were systematically performed on nanocrystalline UO 2 thin films at temperatures ranging from 50 K to 1073 K; grain growth was observed at all temperatures. A combination of manual and machine learning techniques was used to measure and plot grain size evolution against irradiation fluence at various irradiation temperatures. The machine learning method has significantly improved the analysis efficiency and reduced human labors. The grain diameter data were fitted using classical grain growth and thermal spike models to describe grain growth kinetics with and without irradiation effect. Grain growth during low temperature irradiation (≤ 475 K) can be well described by the thermal spike model. Above 475 K, there were additional thermally assisted processes that further accelerate the grain growth. At the highest irradiation temperature about 1075 K, both irradiation-induced dislocation loops and cavities/bubbles were observed to form in the UO 2 . In this report, the effects of irradiation-induced defects on grain growth kinetics are discussed.
The Hall–Petch relationship, according to which the strength of a metal increases as the grain size decreases, has been reported to break down at a critical grain size of around 10 to 15 nanometres. As the grain size decreases beyond this point, the dominant mechanism of deformation switches from a dislocation-mediated process to grain boundary sliding, leading to material softening. In one previous approach, stabilization of grain boundaries through relaxation and molybdenum segregation was used to prevent this softening effect in nickel–molybdenum alloys with grain sizes below 10 nanometres. Here we track in situ the yield stress and deformation texturing of pure nickel samples of various average grain sizes using a diamond anvil cell coupled with radial X-ray diffraction. Our high-pressure experiments reveal continuous strengthening in samples with grain sizes from 200 nanometres down to 3 nanometres, with the strengthening enhanced (rather than reduced) at grain sizes smaller than 20 nanometres. Here, we achieve a yield strength of approximately 4.2 gigapascals in our 3-nanometre-grain-size samples, ten times stronger than that of a commercial nickel material. A maximum flow stress of 10.2 gigapascals is obtained in nickel of grain size 3 nanometres for the pressure range studied here. We see similar patterns of compression strengthening in gold and palladium samples down to the smallest grain sizes. Simulations and transmission electron microscopy reveal that the high strength observed in nickel of grain size 3 nanometres is caused by the superposition of strengthening mechanisms: both partial and full dislocation hardening plus suppression of grain boundary plasticity. These insights contribute the ongoing search for ultrastrong metals via materials engineering.
We report metallic uranium is a leading fuel form for sodium cooled fast reactors as an enabling technology of future nuclear energy systems. Mechanistic understanding of fuel behaviors and kinetics under thermodynamic equilibrium and highly non-equilibrium conditions are essential for evaluating fuel performance. It is important to understand and predict the grain and pore evolutions of metallic fuels under thermal and irradiation conditions. However, very limited data are available on the grain growth kinetics and mechanisms of pure gamma phase uranium. In this paper, the pure gamma uranium pellets with different grain structures were fabricated by combining high-energy ball milling and spark plasma sintering. Isothermal annealing tests were performed to investigate the grain growth behavior of the pure gamma phase uranium with different initial grain sizes. A parabolic relationship in grain growth with time was identified for the submicron-sized (374 nm) sample. In contrast, for the nano-sized (137 nm) sample, the grain growth shows a linear relationship with time. The activation energies of grain growth were determined as 199.5 KJ/mol and 80.6 KJ/mol for nano-sized and submicron-sized grain structures, respectively. For the nano-sized sample, the rate-control step of grain growth is dominated by the triple-junction migration, in which the grain boundary triple junction drags the grain growth, leading to a higher activation energy than the bulk diffusion. The dominating mechanism for the submicron-sized sample is grain boundary diffusion. The mechanistic understanding and critical data obtained on the kinetics of pure uranium phases will be useful to evaluate fuel behavior under thermodynamic equilibrium conditions and develop a high fidelity model to predict fuel performance.
Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. Additionally, a diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.
Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four-dimensional scanning transmission electron microscopy (4D-STEM) datasets. We propose a simplex-based model (SBM) in which each simplex vertex represents the diffraction pattern (DP) of a pure grain, and the simplex edges and interior represent overlapping grains. Our SBM grain identification algorithm operates on the Bragg disk (BD) data matrix distilled from the 4D-STEM data to identify the grain membership at each scan position, together with a BD feature matrix whose columns represent the DPs for each constituent grain, which is important for identifying the crystal structure of each grain. We solve the model using a two-stage algorithm. In Stage 1, we adapt a linear mixing algorithm to estimate an initial BD feature matrix whose columns represent DPs of potentially overlapping grains. Our Stage 2 algorithm incorporates sparsity considerations to transform the initial BD feature matrix so that its columns represent DPs of pure grains. Using simulated datasets with various grain configurations, we demonstrate that SBM recovers both the BD feature matrix and membership maps more accurately than existing methods, even when a grain lacks any pure region and completely overlaps with other grains.
Dynamic grain growth is demonstrated to be much faster than static grain growth in a body-centered-cubic, interstitial-free steel sheet material at 850°C. Dynamic grain growth occurs during concurrent plastic deformation at elevated temperature, whereas static grain growth occurs during static annealing. Grain growth during steady-state plastic flow in tension at 850°C to a true strain of 0.2 at a true-strain rate of 10 -4 s -1 doubled grain size, while static annealing for the same time produced no increase in grain size. This is described as dynamic normal grain growth (DNGG) because no abnormally large grains were observed. The recrystallized microstructure of the steel demonstrated a log-normal distribution of grain sizes. DNGG produced bimodal grain size distributions that deviate from the theoretical expectation of a simple shift to larger sizes during normal growth. The bimodal distributions contained a remnant of small grains that were not consumed during grain growth. DNGG produced a crystallographic texture that is unique from both the recrystallized material and that produced by lattice rotation alone. DNGG strengthened the {111}⟨110⟩ and {111}⟨112⟩ components of the strong γ-fiber component in the original recrystallization texture. Lattice rotation from tensile deformation, by contrast, strengthened the α-fiber components that intersect the original γ-fiber.
Here, a phase-field model for describing dynamic interactions between deformation twins and grain boundaries in a hexagonal close packed (HCP) metal is established. It is applied to simulating the coupled evolution mechanisms of grains and twins in magnesium (Mg) by probing the transmission of $\{10\bar{1}2\}$ deformation twins across grain boundaries. We analyze the effect of the strain relaxation near a grain boundary, which is related to the geometric compatibility arising from the misorientation angle between adjoining grains. Phase-field simulations demonstrate that twin transmission across a grain boundary into a neighboring grain leads to grain boundary migration towards the neighboring grain with a reduced grain boundary width. The preferred nucleation site of new twin variant in the neighboring grain is related to the elastic interaction energy distribution. These predicted transmission behaviors agree well with existing experimental observations and molecular dynamics simulations. By analyzing set of systematic phase-field simulation results, we establish a rotation-angle-related twin variant selection rule for analyzing the transmission of $\{10\bar{1}2\}$ deformation twins across grain boundaries in Mg and its alloys.
Grain boundaries in metallic materials can exist in a wide range of stable and metastable structures. In addition, the properties of a grain boundary may be altered through solute segregation. In this work, we present a formulation that combines the spectrum of embrittling potencies associated with solute segregation with site-occupancy statistics. As a prototype problem, we illustrate the relation between segregation and embrittlement in the case of S segregation to grain boundaries in Ni. To obtain a population of site segregation energies, we perform molecular statics calculations on 378 different symmetric-tilt grain boundaries and their free surface equivalents, using an embedded-atom method interatomic potential developed specifically for studying embrittlement. Our results show that it is important to consider both the energies associated with embrittlement and the probability of occupancy to describe the general embrittling nature of a grain boundary. When analyzed in isolation, certain grain boundaries show large embrittling potencies; however, that effect is diminished when the probability of S segregation to that grain boundary is considered within a polycrystal. We propose a new quantity, the embrittling estimator, which not only categorizes grain boundaries as embrittling or strengthening, but also considers site occupancy probabilities, so that the embrittlement behavior of grain boundaries within a network of grain boundaries can be compared. Finally, we examine the relationship between embrittlement behavior and innate grain boundary properties, such as the free volume, and find statistical evidence that the complex nature of embrittlement cannot be explained by linear correlations with excess volumes or energies. Ultimately, this combined approach provides a theoretical tool to assist grain boundary engineering of metastable alloys.
The growth of an isolated spherical void at and near a grain boundary or a triple junction is studied by means of full-field crystal plasticity simulations with explicit representation of the void. To examine the fundamental aspects of void/grain boundary interactions in these cases, a three-dimensional large-strain elasto-viscoplastic fast-Fourier transform (LS-EVP-FFT) model with axisymmetric tensile periodic boundary conditions was developed. We employ this model to reveal the role of crystallography (grain orientations, grain boundary misorientation and inclination angle with respect to the largest component of the applied stress) and type of loading on void growth. It was found that grain orientation was the primary crystallographic feature governing void growth in strain-rate controlled simulations. For strains small enough to not significantly alter the local geometry, the overall growth of the void in bicrystal boundaries was not strongly affected by grain boundary inclination angle. The relative amount of void growth experienced within each grain in a bicrystal or tri-crystal was strongly dependent on the grain boundary inclination angle. We found a subset of orientations that exhibit faster void growth in bicrystal boundaries than inside the constituent single crystals. Last, we show that when placed primarily in one grain but near the grain boundary, void growth was similar to void growth when placed at the grain boundary. Furthermore, the growing void remained in the original grain and did not grow into the other grain, regardless of crystallographic character.
Though metallic U-Zr fuel has been used in nuclear reactors since the 1960s, many of its fundamental and thermodynamic properties are still unknown. The a-U phase, which has a highly anisotropic crystal structure and physical properties, is present in U-Zr fuel. The character and behavior of a-U grain boundaries will strongly impact fuel thermophysical performance under irradiation. Here, we study the interaction of point defects with grain boundaries, diffusion along grain boundaries, and the predicted diffusional creep behavior of a-U via molecular dynamics. We calculate the segregation energy of vacancies and interstitials to grain boundaries and quantify the biased sink strength of the grain boundaries, and observe that this sink strength is not strongly dependent on the grain boundary orientation. We also find that grain boundary diffusivity is strongly dependent on the grain boundary energy and grain boundary orientation. The presence of point defects within the grain boundary can induce diffusion in grain boundaries with low formation energies and can enhance diffusion in high-energy grain boundaries. We also find that diffusional creep of a-U at prototypical metallic fuel operation conditions is extremely high and could help explain observed metallic fuel swelling behaviors.
Abstract While grain growth is traditionally viewed as a purely thermally driven process, nanocrystalline metals can undergo grain growth under mechanical loads, even at room temperature. We performed a detailed atomistic study of the heterogeneous nature of mechanically accelerated grain growth in a polycrystalline Pt nanowire. Using molecular dynamics simulations, we compared the grain-growth behavior of individual grains during tensile and shear cyclic loading, for three different equivalent strain levels, and at two temperatures. Pure thermal grain growth with no mechanical loading provided a baseline reference case. On average, grains that were already susceptible to thermal grain growth were stimulated to grow faster with mechanical loading, as expected. However, when analyzed on a grain-by-grain basis, the results were far more complex: grains that grew fastest under one stimuli were less accelerated under other stimuli. Even when the magnitude of loading changed, the relative growth of individual grains was distorted. We interpret this complexity from the perspective of superimposed growth mechanisms.
The grain growth behavior of textured Ca-doped alumina is compared to Monte Carlo Potts (MCP) simulations to investigate the effect of anisotropic grain boundary (GB) properties on local boundary migration. Experimentally, the growth of textured Ca-doped alumina results in highly elongated grains. The relative GB energy distribution is measured using the thermal groove method before and after heat treating at 1600°C, finding that high energy GBs are eliminated during grain growth. No significant difference in the GB energy distributions is found between the long and short axes of the elongated grains, suggesting that anisotropic mobility may be responsible for the grain shape. However, MCP simulations with anisotropic mobility as a function of plane inclination do not result in grains with distinct morphologies, regardless of the degree of anisotropy introduced. The final grain shape after grain growth of textured Ca-doped alumina resembles that of the MCP simulations using an anisotropic GB energy as a cosine function of plane inclination. Several energy functions are tested and only those that mathematically impose a torque (second derivative of energy with respect to the plane inclination angle) result in elongated grains. Although area reduction is the dominant energy minimization mechanism, these results suggest that local GB migration is affected by anisotropic GB energy and torque and alternative mechanisms like GB replacement and reorientation.
Effect of dynamic recrystallization (DRX) on the grain refinement and texture modification was studied by conducting a series of hot compression on AZ31B Mg alloy. Processing-grain size-texture maps were established as a function of temperature, strain rate, strain, and the Zener-Hollomon parameter (Z). Moreover, influence of simultaneous changes in the grain size and texture on tensile yield strength and ductility of the hot-worked Mg alloy was studied using the grain size and texture maps established. The processing-grain size map showed that the DRX grain size decreased with the increase in Z, also revealing various characteristics ranging from a grain growth, grain refinement, to a bimodal distribution of ultrafine grains and partially recrystallized grains. The effect of twinning on the grain refinement was also evident at high Z conditions. The processing-texture map revealed that the initial fiber texture was altered significantly to a shear, off-normal, or extension-twin texture with the increase in Z. The Schmid-factor maps were calculated to identify a dominant deformation mode during a subsequent tensile deformation of the hot-worked samples. Finally, a corresponding Hall-Petch relationship for each dominant deformation mode was used to establish tensile yield-strength maps, which agree well with the measured data.
We demonstrate cylindrical island grain bicrystals grown via a hybrid additive manufacturing/directional solidification technique. The island grain bicrystal is formed using a specialized mold in which one grain (the island) is enveloped by another larger grain (the surrounding matrix). The grain boundary plane orientation varies continuously around the circumference of the island grain, making these specimens ideal for probing grain boundary structure–property relations. We report thermal groove angle measurements along the grain boundary in a tin island grain bicrystal which show the grain boundary energy as a function of a single degree of freedom in the five-dimensional grain boundary parameter space.
Cellular automata (CA) models of as-solidified grain structure, originally developed and applied to casting, have become a common means of predicting grain structure resulting from Additive Manufacturing (AM) processes. The majority of these models are based on the decentered octahedron approach, which attempts to correct for the effect of grid anisotropy on the prediction of competitive solidification of dendritic grains. However, AM solidification occurs under cooling rates ($\dot{T}$) and thermal gradients (G) that are orders of magnitude larger than those encountered in casting, and no systematic investigation on the effect of the CA model cell size (Δx) and time step (Δt) on AM microstructure predictions has been performed. Here, in this study, such an investigation is first performed via simulation of individual grains of various crystallographic orientations with a fixed, unidirectional G, showing that CA prediction of the steady-state undercooling matched the expected values based on the interfacial response function at small G and deviated from the expected values at large G. Simulation of competitive growth of multiple grains showed a weakening of the predicted texture as G and Δx became large. Simulation of solidification under AM conditions, where G and $\dot{T}$ vary spatially across the melt pools, showed that not only does grain selection weaken and deviate from expectations at large Δx, but grains with crystallographic $\langle$100$\rangle$ aligned with the grid directions are more adversely affected by the temperature field discontinuities than grains with other crystallographic orientations. Despite the fact that the exact grain competition results depended on Δt, the overall texture development was notably less sensitive to Δt than Δx, provided that a reasonable value of Δt is selected based on the ratio of Δx to the maximum local solidification velocity in the simulation domain. Finally, from the directional solidification and AM simulation results, an analysis of computational cost compared to simulation resolution is performed based on an equation derived to quantify the relatively inaccuracy in grain selection based on the model and temperature field inputs. From this analysis, it is concluded that there is a need for algorithmic improvements to improve CA grain competition accuracy for large G processing conditions as sufficiently small Δx to resolve the necessary competition is intractable for many AM processing conditions.