Energetics of vacancy segregation to  symmetric tilt grain boundaries in bcc tungsten.
ABSTRACT: The harsh irradiation environment poses serious threat to the structural integrity of leading candidate for plasma-facing materials, tungsten (W), in future nuclear fusion reactors. It is thus essential to understand the radiation-induced segregation of native defects and impurities to defect sinks, such as grain boundaries (GBs), by quantifying the segregation energetics. In this work, molecular statics simulations of a range of equilibrium and metastable  symmetric tilt GBs are carried out to explore the energetics of vacancy segregation. We show that the low-angle GBs have larger absorption length scales over their high-angle counterparts. Vacancy sites that are energetically unfavorable for segregation are found in all GBs. The magnitudes of minimum segregation energies for the equilibrium GBs vary from -2.61?eV to -0.76?eV depending on the GB character, while those for the metastable GB states tend to be much lower. The significance of vacancy delocalization in decreasing the vacancy segregation energies and facilitating GB migration has been discussed. Metrics such as GB energy and local stress are used to interpret the simulation results, and correlations between them have been established. This study contributes to the possible application of polycrystalline W under irradiation in advanced nuclear fusion reactors.
Project description:The microstructural response of beryllium after neutron irradiation at various temperatures (643-923?K) was systematically studied using analytical transmission electron microscope that together with outcomes from advanced atomistic modelling provides new insights in the mechanisms of microstructural changes in this material. The most prominent feature of microstructural modification is the formation of gas bubbles, which is revealed at all studied irradiation temperatures. Except for the lowest irradiation temperature, gas bubbles have the shape of thin hexagonal prisms with average height and diameter increasing with temperature. A high number density of small bubbles is observed within grains, while significantly larger bubbles are formed along high-angle grain boundaries (GB). Denuded zones (DZ) nearly free from bubbles are found along both high- and low-angle grain boundaries. Precipitations of secondary phases (mainly intermetallic Al-Fe-Be) were observed inside grains, along dislocation lines and at GBs. EDX analysis has revealed homogeneous segregation of chromium and iron along GBs. The observed features are discussed with respect to the available atomistic modelling results. In particular, we present a plausible reasoning for the abundant formation of gas bubbles on intermetallic precipitates, observation of various thickness of zones denuded in gas bubbles and precipitates, and their relation to the atomic scale diffusion mechanisms of solute-vacancy clusters.
Project description:Interaction of vacancies with grain boundaries (GBs) is involved in many processes occurring in materials, including radiation damage healing, diffusional creep, and solid-state sintering. We analyze a model describing a set of processes occurring at a GB in the presence of a non-equilibrium, non-homogeneous vacancy concentration. Such processes include vacancy diffusion toward, away from, and across the GB, vacancy generation and absorption at the GB, and GB migration. Numerical calculations within this model reveal that the coupling among the different processes gives rise to interesting phenomena, such as vacancy-driven GB motion and accelerated vacancy generation/absorption due to GB motion. The key combinations of the model parameters that control the kinetic regimes of the vacancy-GB interactions are identified via a linear stability analysis. Possible applications and extensions of the model are discussed.
Project description:Material performance is significantly governed by grain boundaries (GBs), a typical crystal defects inside, which often exhibit unique properties due to the structural and chemical inhomogeneity. Here, it is reported direct atomic scale evidence that oxygen vacancies formed in the GBs can modify the local surface oxygen dynamics in CeO2, a key material for fuel cells. The atomic structures and oxygen vacancy concentrations in individual GBs are obtained by electron microscopy and theoretical calculations at atomic scale. Meanwhile, local GB oxygen reduction reactivity is measured by electrochemical strain microscopy. By combining these techniques, it is demonstrated that the GB electrochemical activities are affected by the oxygen vacancy concentrations, which is, on the other hand, determined by the local structural distortions at the GB core region. These results provide critical understanding of GB properties down to atomic scale, and new perspectives on the development strategies of high performance electrochemical devices for solid oxide fuel cells.
Project description:This work presents a comprehensive and detailed ab initio study of interactions between the tilt 5(210) grain boundary (GB), impurities X (X = Al, Si) and vacancies (Va) in ferromagnetic fcc nickel. To obtain reliable results, two methods of structure relaxation were employed: the automatic full relaxation and the finding of the minimum energy with respect to the lattice dimensions perpendicular to the GB plane and positions of atoms. Both methods provide comparable results. The analyses of the following phenomena are provided: the influence of the lattice defects on structural properties of material such as lattice parameters, the volume per atom, interlayer distances and atomic positions; the energies of formation of particular structures with respect to the standard element reference states; the stabilization/destabilization effects of impurities (in substitutional (s) as well as in tetragonal (iT) and octahedral (iO) interstitial positions) and of vacancies in both the bulk material and material with GBs; a possibility of recombination of Si(i) +Va defect to Si(s) one with respect to the Va position; the total energy of formation of GB and Va; the binding energies between the lattice defects and their combinations; impurity segregation energies and the effect of Va on them; magnetic characteristics in the presence of impurities, vacancies and GBs. As there is very little experimental information on the interaction between impurities, vacancies and GBs in fcc nickel, most of the present results are theoretical predictions, which may motivate future experimental work.
Project description:High entropy alloys (HEAs) have emerged as a new class of multicomponent materials, which have potential for high temperature applications. Phase stability and creep deformation, two key selection criteria for high temperature materials, are predominantly influenced by the diffusion of constituent elements along the grain boundaries (GBs). For the first time, GB diffusion of Ni in chemically homogeneous CoCrFeNi and CoCrFeMnNi HEAs is measured by radiotracer analysis using the 63Ni isotope. Atom probe tomography confirmed the absence of elemental segregation at GBs that allowed reliable estimation of the GB width to be about 0.5?nm. Our GB diffusion measurements prove that a mere increase in number of constituent elements does not lower the diffusion rates in HEAs, but the nature of added constituents plays a more decisive role. The GB energies in both HEAs are estimated at about 0.8-0.9?J/m2, they are found to increase significantly with temperature and the effect is more pronounced for the CoCrFeMnNi alloy.
Project description:We provide the dataset of the vacancy (interstitial) formation energy, segregation energy, diffusion barrier, vacancy-interstitial annihilation barrier near the grain boundary (GB) in bcc-iron and also the corresponding interactive range. The vacancy-interstitial annihilation mechanisms in the bulk, near the GB and at the GB at across scales were given.
Project description:Level energies are reported for Si V, Si VI, Si VII, Si VIII, Si IX, Si X, Si XI and Si XII. The energies have been calculated with the relativistic Multi-Reference Møller-Plesset Perturbation Theory method and include valence and K-vacancy states with nl up to 5f. The accuracy of the calculated level energies is established by comparison with the recommended data listed in the National Institute of Standards and Technology (NIST) on-line database. The average deviation of valence level energies ranges from 0.20 eV in Si V to 0.04 eV in Si XII. For K-vacancy states, the available values recommended in the NIST database are limited to Si XII and Si XIII. The average energy deviation is below 0.3 eV for K-vacancy states. The extensive and accurate dataset presented here greatly augments the amount of available reference level energies. We expect our data to ease the line identification of L-shell ions of Si in celestial sources and laboratory generated plasmas, and to serve as energy references in the absence of more accurate laboratory measurements.
Project description:The loss of ductility with temperature has been widely observed in tensile tests of single-phase face-centered cubic structured high-entropy alloys (HEAs). However, the fundamental mechanism for such a ductility loss remains unknown. Here, we show that ductility loss in the CrMnFeCoNi HEA upon deformation at intermediate temperatures is correlated with cracking at grain boundaries (GBs). Nanoclustering Cr, Ni, and Mn separately at GBs, as detected by atom probe tomography, reduces GB cohesion and promotes crack initiation along GBs. We further demonstrated a GB segregation engineering strategy to avoid ductility loss by shifting the fast segregation of principal elements from GBs into preexisting Cr-rich secondary phases. We believe that GB decohesion by nanoclustering multiprincipal elements is a common phenomenon in HEAs. This study not only provides insights into understanding ductility loss but also offers a strategy for tailoring ductility-temperature relations in HEAs.
Project description:Calculated level energies for valence and K-vacancy states are provided for the ion series S VII - S XIV and Ar IX - Ar XVI. The calculations were performed with the relativistic Multi-Reference Mxller-Plesset Perturbation Theory method (MR-MP). The data set includes all the level energies with configurations 1s 22(s, p) q , 1s 22(s, p) q-1 nl, 1s 12(s, p) q+1, 1s 12(s, p) q nl, 2(s, p) q+2 and 2(s, p) q+1 nl, where 1 ? q ? 8, n ? 5 and l ? 3. We have compared our results with data from the National Institute of Standards and Technology (NIST) on-line database and with previous calculations. The average deviation of valence level energies ranges from 0.16 eV in Ne-like ions to 0.01 eV in Li-like ions, showing that the present MR-MP valence level energies are highly accurate. In the case of K-vacancy states, the deviation is generally below 0.3 eV for Li-like S XIV and Ar XVI. The deviation for K-vacancy energies in other L-shell ions (Be-, B-, C-, N- and O-like Ar ions) is higher but likely because the NIST-recommended values have a higher uncertainty. The data set includes many n = 4 and n = 5 valence and K-vacancy levels in L-shell ions of S and Ar not previously reported. The data can be used for line identification and modeling of L-shell ions of S and Ar in astrophysical and laboratory-generated plasmas, and as energy references in the absence of more accurate laboratory measurements.
Project description:The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of r2 ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction.