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Grain boundaries can undergo phase-like transitions, called complexion transitions, in which their structure, composition, and properties change discontinuously as temperature, bulk composition, and other parameters are varied. Grain boundary complexion transitions can lead to rapid changes in the macroscopic properties of polycrystalline metals and ceramics and are responsible for a variety of materials phenomena as diverse as activated sintering and liquid-metal embrittlement. The property changes caused by grain boundary complexion transitions can be beneficial or detrimental. Grain boundary complexion engineering exploits beneficial complexion transitions to improve the processing, properties, and performance of materials. Here, we review the thermodynamic fundamentals of grain boundary complexion transitions, highlight the strongest experimental and computationalevidence for these transitions, clarify a number of important misconceptions, discuss the advantages of grain boundary complexion engineering, and summarize existing research challenges.

The field of multi-principal element or (single-phase) high-entropy (HE) alloys has recently seen exponential growth as these systems represent a paradigm shift in alloy development, in some cases exhibiting unexpected structures and superior mechanical properties. However, the identification of promising HE alloys presents a daunting challenge given the associated vastness of the chemistry/composition space. We describe here a supervised learning strategy for the efficient screening of HE alloys that combines two complementary tools, namely: (1) a multiple regression analysis and its generalization, a canonical-correlation analysis (CCA) and (2) a genetic algorithm (GA) with a CCA-inspired fitness function. These tools permit the identification of promising multi-principal element alloys. We implement this procedure using a database for which mechanical property information exists and highlight new alloys having high hardnesses. Our methodology is validated by comparing predicted hardnesses with alloys fabricated by arc-melting, identifying alloys having very high measured hardnesses., The identification of high entropy alloys is challenging given the vastness of the compositional space associated with these systems. Here the authors propose a supervised learning strategy for the efficient screening of high entropy alloys, whose hardness predictions are validated by experiments.

In recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

Patch models have been employed to describe anisotropic interactions in disparate soft matter systems. In this work, we present a unified study of a patch model to explore particle self-assembly in both monodisperse and polydisperse systems, with applications to both proteins and colloids. In the first case, we obtained a temperature-density phase diagram for a model of the protein polyglutamine from Monte Carlo simulations. These simulations evinced clusters in the gas phase and, via a comparison with the corresponding coarse-grained PLUM model for this system, we verified that dense clustering in the gas phase falls into the supersaturation region of the saturation curve. In the second case, we have investigated the effect of size polydispersity on the phase behavior of binary colloidal mixtures. It was found that the width of gas-liquid phase coexistence increases with increasing polydispersity and that, in addition to the aforementioned particle clustering, small particles decorate patches on large particles, thereby creating large-particle bridges. Our aim is to compare and contrast self-assembly in these two prototypical systems.

While solid-state reactions are employed for the processing of complex materials, the microstructural evolution that initiates from a compositionally inhomogeneous patterned structure has received little to no attention. For example, it has been recognized recently that it is possible to achieve such structures in systems having entropy-stabilized line compounds that are thermodynamically stable only above a certain critical temperature. To obtain a better understanding of the kinetics and product microstructures associated with reactions that initiate on a template, we employ here a reaction-diffusion formalism to model the microstructural evolution of the product phase produced via solid-state reaction from a starting, bi-phasic template. In particular, we connect the spatio-temporal evolution of the product phase with the geometry and chemistry of the starting duplex structure. The formalism employed here was developed for chemical systems and, in this context, we show that it enables an understanding of microstructural evolution associated with solid-state reactions. While we consider here idealized examples to highlight underlying processes, we also discuss how one can use the results to select judiciously templates to promote the formation of desirable microstructures and to quantify important experimental kinetic parameters that dictate observed patterns. Finally, we describe the use of our simulation methodology to extract important kinetic information from experimentally-generated product-phase microstructures resulting from solid-state reactions.

It is often advantageous to display material properties relationships in the form of charts that highlight important correlations and thereby enhance our understanding of materials behavior and facilitate materials selection. Unfortunately, in many cases, these correlations are highly multidimensional in nature, and one typically employs low-dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employ methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials data and to extract useful relationships among properties. We illustrate the utility of this approach by the construction and systematic analysis of multidimensional materials properties charts for metallic and ceramic systems. These charts simplify the description of high-dimensional geometry, enable dimensional reduction and the identification of significant property correlations and underline distinctions among different materials classes. Multidimensional materials property charts can be constructed to better identify the relationship among different properties. When investigating the behavior of materials, the relationship between different properties is usually established through analysis of two-dimensional charts. The correlation between properties is usually far more complex, but visualizing high-dimensional materials data in a way that enables meaningful comparisons is not easy. By employing methods of data analytics with a visualization strategy known as parallel coordinates, Jeffrey Rickman from Lehigh University demonstrates a new way to represent multidimensional materials data that allows useful relationships among properties to be extracted. The author demonstrates the power of this framework by constructing and analysing multidimensional materials properties charts for metallic and ceramic systems, showing that it provides a powerful tool for identifying property relationships.

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables. It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex, multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated. One shortcoming of the canonical correlation analysis, however, is that it provides only a linear combination of variables that maximizes these correlations. With this in mind, we describe here a versatile, Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations. We demonstrate that our approach leads to a substantial enhancement of correlations, as illustrated by two experimental applications of substantial interest to the materials science community, namely: (1) determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas, and (2) relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe2 absorbers. Finally, we describe how this approach facilitates experimental planning and process control. A method for quantifying non-linear relationships provides insight into the connections between microstructure and properties of materials. Canonical correlation analysis is a common technique used to quantify the relationship between two sets of variables but it is often difficult to apply when the relationships are non-linear. An international team of researchers led by Jeffrey Rickman from Lehigh University now present a Monte-Carlo-based extension of canonical correlation analysis that can be applied to scenarios where non-linear variable dependencies are likely. They demonstrate this approach by establishing correlations between the variables responsible for abnormal grain growth in a ceramic oxide, as well as the variables that are most important in connecting the microstructure to the electrical and optoelectronic properties of certain solar cells, showing the range of materials systems that this approach could be used for.

Grain boundaries and other interfaces can undergo complexion transitions from one thermodynamic state to another, resulting in discontinuous changes in interface properties such as diffusivity, mobility, and cohesive strength. The kinetics of such complexion transitions has been largely overlooked until recently. Just as with bulk phase transformations, complexion transition kinetics can be represented on time-temperature-transformation (TTT) diagrams. An experimental complexion TTT diagram is presented here for polycrystalline Eu-doped spinel annealed at 1400–1800 °C. This material developed a microstructure with a bimodal grain size distribution, indicating that a complexion transition occurs within this temperature range. The time and temperature dependence of this complexion transition was analyzed and used to produce a grain-boundary complexion TTT diagram for this system. Complexion TTT diagrams have the potential to be remarkably useful tools for manipulating the properties of internal interfaces in polycrystalline metals and ceramics. The development of experimental complexion TTT diagrams is likely to have an important impact on the field of grain-boundary engineering, and hence the development of these experimental diagrams should be an intense area of focus in the coming years.

We review various simplified models that have been advanced to describe layering (complexion) transitions at grain boundaries in multicomponent solids. In particular, we first outline lattice-gas, off-lattice atomistic and thermodynamic models that have been employed to investigate phase-like behavior at segregated grain boundaries. The results of these investigations are summarized in the form of complexion diagrams in different thermodynamic planes, and we highlight important features of these diagrams, such as complexion transition lines and critical points. Finally, we describe current issues and provide a future outlook.

We employ semi-grand canonical Monte Carlo simulation to investigate layering transitions at grain boundaries in a prototypical binary alloy. We demonstrate the existence of such transitions among various interfacial states and examine the role of elastic fields in dictating state equilibria. The results of these studies are summarized in the form of diagrams that highlight interfacial state coexistence in this system. Finally, we examine the impact of layering transitions on the phononic properties of the system, as given by the specific heat and, by extension, the thermal conductivity. Thus, it is suggested that by inducing interfacial layering transitions via changes in temperature or pressure, one can thereby engineer thermodynamic and transport properties in materials.

Abnormal grain growth, the enlargement of a minority of grains in a polycrystal at the expense of the surrounding grains, occurs in both metallic and ceramic materials and can have a profound impact on their mechanical and electrical properties. Somewhat surprisingly then, there is little consensus as to which specific microstructural features provide a signature of abnormal growth. Indeed, some workers describe this phenomenon in terms of a bimodal grain size distribution, often without justification, while others focus on very few, elongated grains. Using specialty alumina (i.e., high-purity aluminum oxide with tailored impurity composition) as our prototype, we describe here a set of practical maps and metrics that are useful in quantifying various microstructural features that are associated with abnormal grain growth. These maps provide a visual “fingerprint” of abnormal growth, while the metrics permit the design of processing routes to obtain desired microstructures. We then present an application of correlation analysis that illustrates the efficacy of data analytics in quantifying which input (i.e., processing) variables exert the strongest influence on abnormal grain growth. Finally, we outline the use of this methodology to examine correlations among processing variables and the thermomechanical and kinetic properties of materials (e.g., strength, hardness, thermal conductivity, etc.).

The study of high-entropy (HE) alloys has seen dramatic growth in recent years as, in some cases, these systems can exhibit exceptional properties, including enhanced oxidation resistance, superior mechanical properties, and desirable magnetic properties. The identification of promising HE alloys is, however, extremely challenging due to the extraordinarily large number of distinct systems that may be fabricated from the available palette of elements. For this reason, machine learning strategies have been employed to reduce the size of the associated chemistry/composition space. In this review, we outline several computational strategies that have led to the identification of useful alloys and discuss the relative merits and shortcomings of these approaches. We also present short tutorials illustrating the use of selected computational approaches to HE characterization and design.

In this paper, we study the early stages of aggregation of a model of polyglutamine (polyQ) for different repeat lengths (number of glutamine amino acid groups in the chain). In particular, we use the Large-scale Atomic/Molecular Massively Parallel Simulator to study a generic coarse-grained model proposed by Bereau and Deserno. We focus on the primary nucleation mechanism involved and find that our results for the initial self-assembly process are consistent with the two-dimensional classical nucleation theory of Kashchiev and Auer. More specifically, we find that with decreasing supersaturation, the oligomer fibril (protofibril) transforms from a one-dimensional β sheet to two-, three-, and higher layer β sheets as the critical nucleus size increases. We also show that the results are consistent with several predictions of their theory, including the dependence of the critical nucleus size on the supersaturation. Our results for the time dependence of the mass aggregation are in reasonable agreement with an approximate analytical solution of the filament theory by Knowles and collaborators that corresponds to an additional secondary nucleation arising from filament fragmentation. Finally, we study the dependence of the critical nucleus size on the repeat length of polyQ. We find that for the larger length polyglutamine chain that we study, the critical nucleus is a monomer, in agreement with experiment and in contrast to the case for the smaller chain, for which the smallest critical nucleus size is four.

In recent years the statistical mechanics of non-spherical molecules, such as polypeptide chains and protein molecules, has garnered considerable attention as the phase behavior of these systems has important scientific and health implications. More recently, it has been realized that surface binding may have a considerable impact on this behavior. With this in mind, we examine here the role of surface interactions on the phase behavior of Y-shaped molecules (a simplified model of immunoglobulin) using grand-canonical Monte Carlo simulation. In particular, we investigate the critical behavior of a system of such molecules on a hexagonal lattice using histogram reweighting, multicanonical sampling, and finite-size scaling. After obtaining the critical properties of the three-dimensional bulk system, we investigate the impact of a patterned solid surface on these properties as a function of patterning geometry and surface interaction strength. Our results suggest avenues for tailoring phase behavior by selectively controlling surface characteristics., This work was supported by a grant from the G. Harold and Leila Y. Mathers Foundation (541974). RT acknowledges financial support from EU (FEDER) and the Spanish MINECO under Grant INTENSE@COSYP (FIS2012-30634). DH acknowledges financial support from NSF PHY-0849416 and PHY-1359195.

The kinetics of oxygen grain-boundary diffusion in alumina was studied as a function of HfO2 concentration. The oxidation of Ni marker particles to NiAl2O4 spinel was utilized to delineate the position of the oxidation front. The HfO2 doping levels spanned the solubility limit, ranging from 100 to 2000 ppm (Hf:Al concentration). For each dopant level, the parabolic rate constant (k) was determined for oxidation anneals carried out at 1400°C. Relative to undoped alumina, HfO2 doping slowed the oxygen transport kinetics by a factor of approximately 3–8, depending on the dopant concentration. At 1400°C, the solubility limit of HfO2 was found to be between 100 and 200 ppm. The results showed that the level of benefit saturated at the dopant level corresponding to the solubility limit (sollim), where 100 ppm

We describe a methodology for calculating approximate, yet accurate analytical expressions for the probability density function of grain diameter as obtained from experimental microstructures. This methodology relies on a novel cumulant expansion that is tailored to the lognormal distribution and provides a systematic description of departures from lognormality. We test our methodology by characterizing two data sets obtained from the microstructures associated with polycrystalline, high-purity Al2O3 samples. The utillity of this approach is demonstrated by a detailed statistical analysis.

We employ Gibbs-ensemble Monte Carlo computer simulation to assess the impact of shape anisotropy and particle interaction anisotropy on the phase behavior of a colloidal (or, by extension, protein) fluid comprising patchy ellipsoidal particles, with an emphasis on critical behavior. More specifically, we obtain the fluid-fluid equilibrium phase diagram of hard prolate ellipsoids having Kern-Frenkel surface patches under a variety of conditions and study the critical behavior of these fluids as a function of particle shape parameters. It is found that the dependence of the critical temperature on aspect ratio for particles having the same volume can be described approximately in terms of patch solid angles. In addition, ordering in the fluid that is associated with particle elongation is also found to be an important factor in dictating phase behavior.

Quantitative kinetic models for the A1 to L10 transformation in FePt and related ternary alloy films are derived for three nucleation conditions for both isothermal and constant heating rate annealing. The nucleation conditions are: 1) continuous nucleation, 2) burst nucleation, and 3) pre-existing nuclei. For the first nucleation condition, new nuclei are formed continuously throughout the transformation. For the second nucleation condition under isothermal annealing, nucleation is constrained to occur at a constant rate over only a short period of time. In the limit of this time period going to zero, the nucleation sites are saturated very rapidly and nucleation can be considered to have occurred as a burst. For the third nucleation condition, the nuclei are pre-existing, having formed prior to the start of the transformation. No new nuclei form during the transformation. To identify the nucleation condition that best describes the transformation in FePt and related ternary alloy films, model predictions must be compared with experimental kinetic results.

Mechanical alloying was employed to produce a nanostructured Mo25Nb25Ta25W25 multi-principal element alloy (MPEA) with enhanced mechanical properties. Overall, a 400% increase in hardness was achieved, as compared to similar cast alloys, via mechanical alloying and optimized long-term annealing treatments. Furthermore, advanced characterization, including aberration-corrected scanning transmission electron microscopy, was conducted to elucidate processing-structure-property relationships in which it was determined that, although the introduction of impurities via mechanical alloying is common and thought to be deleterious, impurities can lead to an impressive enhancement of mechanical properties. More specifically, in this study, Fe and N impurities resulted in the formation of nanoscale, ceramic secondary phases. The observed strengthening was attributed, at least in part, to the ceramic impurity phases. Overall, we suggest that a deliberate doping strategy may be employed in the future to tailor MPEA chemistry and thereby achieve superior mechanical properties.

We investigate numerically a series of layering transitions associated with grain-boundary segregation in a lattice-gas model of a binary alloy. By examining the dependence of the excess composition on temperature and relative chemical potential, we find a series of first-order layering transitions that depend on the nature of the grain boundary. Diagrams are constructed to illustrate the richness of the phase-like behavior (complexion transitions) and its dependence on grain misorientation and stress. Finally, the connection between the thermodynamics of this prototypical model and recent observations of complexion transitions associated with interfaces in metals and ceramics is explored.

The L10 phase in FePt films with compositions in the range of 47-50 at.%Pt is found to form more readily (lower kinetic ordering temperatures), to have a higher degree of tetragonality (lower c/a), to be more perfectly ordered (higher values of the order parameter) and to have higher values of magnetocrystalline anisotropy constant compared to compositions outside of this range. It is argued that the ease of L10 formation, as measured by the kinetic ordering temperature, and the more perfect ordering of the L10 phase, as measured by the order parameter, are related to the higher atomic mobilities in films with compositions in this optimal range. Comparison of experimental results from isothermal kinetic experiments with predicted results using kinetic data from nonisothermal experiments and different assumptions regarding the nucleation of the L10 phase shows that the L10 formation kinetics is best described by growth from a fixed density of preexisting nuclei (athermal nucleation).

Aberration-corrected high-angle annular dark-field imaging was used to provide atomic-resolution imaging of the Hf distribution within grain boundaries in polycrystalline alumina. In general, the projected image gave the appearance of multiple layers of segregating Hf ions. However, by combining image simulation with the results of through-focus imaging, it was demonstrated that the observations resulted from atomic layer faceting of the grain surface, and that the Hf segregation in each grain was in fact confined to the outermost cation layer.

We demonstrate that the time evolution of a first-order phase transition may be described quite generally in terms of the statistics of point processes, thereby providing an intuitive framework for visualizing transition kinetics. A number of attractive and repulsive nucleation scenarios is examined followed by isotropic domain growth at a constant rate This description holds for both uncorrelated and correlated nuclei, and may be employed to calculate the nonequilibrium, n-point spatiotemporal correlations that characterize the transition. Furthermore, it is shown that the interpretation of the one-point function in terms of a stretched-exponential, Kolmogorov-Johnson-Mehl-Avrami result is problematic in the case of correlated nuclei, but that the calculation of higher-order correlation functions permits one to distinguish among various nucleation scenarios.

There has been considerable recent interest in the self-assembly and phase behavior of models of colloidal and protein particles with anisotropic interactions. One example of particular interest is amelogenin, an important protein involved in the formation of dental enamel. Amelogenin is primarily hydrophobic with a 25-residue charged C-terminus tail. This protein undergoes a hierarchical assembly process that is crucial to mineral deposition, and experimental work has demonstrated that the deletion of the C-terminus tail prevents this self-assembly. A simplified model of amelogenin has been proposed in which the protein is treated as a hydrophobic sphere, interacting via the Asakura-Oosawa (AO) potential, with a tethered point charge on its surface. In this paper, we examine the effect of the Coulomb interaction between the point charges in altering the phase diagram of the AO model. For the parameter case specific to amelogenin, we find that the previous in vitro experimental and model conditions correspond to the system being near the low-density edge of the metastable region of the phase diagram. Our study illustrates more generally the importance of understanding the phase diagram for proteins, in that the kinetic pathway for self-assembly and the resulting aggregate morphology depends on the location of the initial state in the phase diagram.

Tracer diffusion in an alloy in which the trajectories of one of the species is biased is examined as a model of mass transport with attendant segregation to extended defects (e.g., dynamic strain ageing, grain-boundary segregation). More specifically, we employ Monte Carlo simulation to describe the nonequilibrium diffusive behavior of the components of a two-dimensional lattice gas comprising A and B atoms wherein one of the species ( B ) interacts with randomly distributed line defects to create equilibrium atmospheres at late times. Various kinetic assumptions and defect densities are explored to highlight the role of B -atom mobility and defect interaction strength on the transport behavior of the A atoms. From the calculated instantaneous diffusivity, several diffusive regimes are then identified and related to evolving segregation profiles and, in particular, to the free area available for diffusion.

The evolution of a first-order phase transition in a non-Euclidean space, namely the surface of a sphere, is examined to highlight the role of geometry and spatial confinement on transition kinetics and the associated product microstructure. This is accomplished by investigating spatio-temporal correlations in the transforming system and geometrical features in the transformed microstructure via the calculation of non-equilibrium correlation functions and microstructural descriptors (i.e., moments of the grain-area distribution). From this analysis, the usual Kolmogorov–Johnson–Mehl–Avrami (KJMA) analysis of transformation kinetics and the resulting microstructural tessellation are generalized to this spherical geometry. Computer simulations of nucleation and growth processes are also employed to validate the theoretical description. Finally, our results are extended to characterize more complex nucleation conditions and geometries.

Novel wedge-geometry, dual-layer alumina samples, both undoped and 500 ppm Y3+-doped, were studied in the temperature regime 1250–1400 °C to determine the effect of Y3+ on oxygen grain-boundary transport in alumina. The samples consisted of a wedge-shaped, single-phase alumina top layer, diffusion bonded to an alumina/Ni substrate containing a fine, uniform dispersion of Ni marker particles (0.5 vol.%). The extent of the alumina spinel oxidation layer was measured as a function of the wedge thickness for a series of heat-treatment conditions. Models of the transport behavior were used to derive values for the rate constants (k) in both the alumina top layer and the alumina/Ni substrate. It was found that the presence of yttrium slows oxygen grain-boundary diffusion in alumina by a factor of ∼5 (at 1300 °C), and increases the corresponding activation enthalpy for oxidation from 407 ± 20 to 486 ± 34 kJ mol−1. Microstructural observations suggested that yttrium also slows Ni outward diffusion. A comparison of the different k values revealed that, at 1300 °C, the presence of Ni alone enhances transport by a factor of ∼2 relative to undoped alumina.

The loss of mechanical stability is a general phenomenon that underlies a wide variety of features in the behavior of crystalline materials. In this paper, we present an instability criterion that is of general applicability, subject to the restriction of low temperatures. The criterion is applied to two situations, that of hydrostatically induced cavitation and of quasi-brittle crack growth due to mode I loading. As judged by the results of independent molecular statics simulations, the predictions of the criterion are quite accurate.

The grain-boundary transport of oxygen in polycrystalline α-Al2O3 (undoped and 500 ppm Y3+-doped) was studied in the temperature regime of 1100°–1500°C by monitoring the oxidation of a fine, uniform dispersion of Ni marker particles (0.5 vol%). The annealing treatments were carried out in a high-purity O2 atmosphere (>99.5%). The Ni particles, which are visibly oxidized to nickel aluminate spinel, were used to determine the depth of oxygen penetration. The thickness of the reaction zone was measured as a function of heat-treatment time and temperature, and a comparison of the oxidation rate constants and activation energies for undoped and Y3+-doped alumina was made. The results indicate that the presence of Y3+ slows oxygen grain-boundary transport in alumina by a variable factor of from 15 to 3 in the temperature regime of 1100°–1500°C. The values of the activation energy for undoped and Y3+-doped alumina were determined to be 430±40 and 497±8 kJ/mol, respectively.

In this paper, we design and implement a variety of parallel algorithms for both sweep spin selection and random spin selection. We analyze our parallel algorithms on LogP, a portable and general parallel machine model. We then obtain rigorous theoretical runtime results on LogP for all the parallel algorithms. Moreover, a guiding equation is derived for choosing data layouts (blocked vs. stripped) for sweep spin selection. In regard to random spin selection, we are able to develop parallel algorithms with efficient communication schemes. We introduce two novel schemes, namely the FML scheme and the ?-scheme. We analyze randomness of our schemes using statistical methods and provide comparisons between the different schemes.

The diffusive transport of chromium in both pure and Y-doped fine-grained alumina has been investigated over the temperature range 12501–16501C. From a quantitative assessment of the chromium diffusion profile in alumina, as obtained from electron microprobe analysis, it was found that yttrium doping retards cation diffusion in the grain-boundary regime by over an order of magnitude. The Arrhenius equations for the undoped and Y-doped samples were determined to be: dDb 5 (4.777 0.24) � 10 � 7 exp (� 264.78747.68 (kJ/mol)/RT)(cm 3 /s) and dDb 5 (6.8770.18) � 10 � 8 exp (� 284.91742.57 (kJ/mol)/ RT)(cm 3 /s), respectively. Finally, to elucidate the mechanism for this retardation, the impact of yttrium doping on diffusion activation energies and prefactors was examined.

A three-dimensional, Potts model of liquid phase sintering in a system with full solid wetting was introduced in order to investigate the coarsening kinetics and microstructures associated with this process. Kinetic Monte Carlo simulation was used to probe the coarsening dynamics and obtain the properties of solid particles, including the volume of critical nuclei and the distribution of particle sizes as a function of time. It was found that the average particle volume increased linearly with time and that the particle size distributions were consistent with those obtained experimentally, as in the W–Ni–Fe and Sn–Pb systems. In obtaining these results careful consideration was given to the role of the initial microstructural features in the subsequent evolution of the system.

We employ Monte Carlo simulation to examine encapsulation in a system comprising Janus oblate spheroids and isotropic spheres. More specifically, the impact of variations in temperature, particle size, inter-particle interaction range, and strength is examined for a system in which the spheroids act as the encapsulating agents and the spheres as the encapsulated guests. In this picture, particle interactions are described by a quasi-square-well patch model. This study highlights the environmental adaptation and selectivity of the encapsulation system to changes in temperature and guest particle size, respectively. Moreover, we identify an important range in parameter space where encapsulation is favored, as summarized by an encapsulation map. Finally, we discuss the generalization of our results to systems having a wide range of particle geometries.

We discuss the challenges and controversies involved in obtaining a coarse-grained description of dislocation energetics and dynamics. We start by outlining the structural and dynamical considerations, specific to dislocations, that are associated with homogenization. We then briefly review several existing strategies. Particular attention is paid to the role of dislocation substructure, critical in determining mechanical response, in a coarse-grained representation. Finally, promising future research directions are highlighted.

The effect of iron and iron/yttrium codoping on the densification and grain growth of ultra high-purity (99.995%) fine-grained alumina has been studied. The experiments were carried out under both oxidizing (flowing air) and reducing conditions (N2/H2 mixture, pO2∼5.1 × 10−14). For studies carried out in air, relative to undoped alumina, the addition of 1000 ppm Fe was found to reduce the densification rate by a factor of 5 and also retard the grain growth rate. This result, which was consistent with tensile creep data obtained in a separate study, was attributed to the retardation of grain-boundary diffusive processes by segregating Fe(III) ions. In contrast, under reducing conditions the 1000 ppm Fe- doped samples exhibited an increase in the densification rate of 2.5 orders of magnitude over that of the undoped samples. In the case of the codoped compositions (1000 ppm Fe/1000 ppm Y), for heat treatment in air, the densification behavior did not differ significantly from that of samples singly doped with Y (1000 ppm). However, under reducing conditions, the presence of the Fe2+ in the samples appeared to compensate for the retarding effect of the yttrium, such that the densification rate of the codoped samples was comparable with that of the undoped material. A mechanism based on compensating point defects is invoked to rationalize the more rapid kinetics under reducing conditions.

The tensile creep behavior of two rare-earth dopant systems, lanthanum- and yttrium-doped alumina, are compared and contrasted in order to better understand the role of oversized, isovalent cation dopants in determining creep behavior. It was found that, despite some microstructural differences, these systems displayed qualitatively a similar improvement in creep resistance, supporting the hypothesis that creep is strongly influenced by segregation. Differences in primary creep behavior and activation energy for steady-state creep were, however, observed for these systems. Given these results, it is expected that creep behavior can be further optimized by adjusting the dopant level and by controlling the microstructure.

Oversized dopant ions such as yttrium and lanthanum segregate to grain boundaries and reduce the tensile creep rate of α-Al 2 O 3 by 2-3 orders of magnitude. One explanation for this behavior is that the oversized segregants give rise to a site-blocking effect for grain boundary diffusion. It has also been speculated that the dopant ions modify the grain boundary structure in alumina and reduce the creep rate by promoting the formation of special (e.g., coincidence site lattice (CSL)) grain boundaries. In order to test the latter hypothesis, we have used electron backscattered Kikuchi diffraction to characterize the misorientation and special grain boundary distribution for undoped and 1000-ppm-yttrium-doped alumina. The results show that the grain boundary structure in alumina (as characterized by the frequency of selected CSLs and misorientation distribution) was not significantly changed by the addition of yttrium, indicating that creep retardation results mainly from site-blocking.

The tensile creep behavior of both singly and multiply doped alumina samples has been investigated in order to understand better the impact of dopant segregation to grain boundaries on observed creep resistance. Previous studies have suggested that the segregation of the oversized dopant ions reduces the grain boundary diffusivity and thus the creep rate. The aims of the present work are to examine the possibly beneficial effects of selective codoping in enhancing creep resistance, and to elucidate the role (if any) of precipitates in creep inhibition. The specific singly and codoped systems considered in this work were as follows: hot-pressed alumina samples containing nominally (i) 100 ppm zirconium, (ii) 100 ppm neodymium, (iii) 100 ppm zirconium codoped with either 100, 350, or 1000 ppm neodymium, (iv) 100 ppm zirconium codoped with 1000 ppm scandium. Microchemical mapping using secondary ion mass spectrometry revealed direct evidence of cosegregation of the dopant ions to grain boundaries. Tensile creep tests were carried out in the temperature range of 1200-1350°C, utilizing stresses ranging from 20 to 100 MPa. In the case of the Nd/Zr codoped alumina, it was found that the creep rate decreased by 2 to 3 orders of magnitude relative to undoped alumina. This improvement was greater than that achieved by doping with either Nd or Zr alone, and demonstrates that the incorporation of ions of differing sizes may be beneficial. The observed enhancement in creep resistance was obtained for compositions both above and below the solubility limit of Nd in alumina; hence the phenomenon is primarily a solid solution effect.

High-spatial-resolution analytical electron microscopy using energy-dispersive X-ray (EDX) and electron energy-loss spectrometry (EELS) of yttrium- and lanthanum-doped Al2O3 has been conducted to ascertain the level of segregation of these impurities to grain boundaries. Line profile analyses indicate that the segregation is confined to a layer thickness of