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Paper des Monats
- ja (9)
A giant Zn segregation transition is revealed using CALPHAD-integrated density-based modeling of segregation into Fe grain boundaries (GBs). The results show that above a threshold of only a few atomic percent Zn in the alloy, a substantial amount of up to 60 at.% Zn can segregate to the GB. We found that the amount of segregation abruptly increases with decreasing temperature, while the Zn content in the alloy required for triggering the segregation transition decreases. Direct evidence of the Zn segregation transition is obtained using high-resolution scanning transmission electron microscopy. Base on the model, we trace the origin of the segregation transition back to the low cohesive energy of Zn and a miscibility gap in Fe-Zn GB, arising from the magnetic ordering effect, which is confirmed by ab-initio calculations. We also show that the massive Zn segregation resulting from the segregation transition greatly assists with liquid wetting and reduces the work of separation along the GB. The current predictions suggest that control over Zn segregation, by both alloy design and optimizing the galvanization and welding processes, may offer preventive strategies against liquid metal embrittlement.
In the past two decades, numerous relaxation or physical aging experiments of metallic glasses have revealed signatures of intermittent atomic-scale processes. Revealed via intensity cross-correlations from coherent scattering using X-ray photon correlation spectroscopy (XPCS), the observed abrupt changes in the time-domain of atomic motion does not fit the picture of gradual slowing down of relaxation times and their origin continues to remain unclear. Using a binary Lennard-Jones model glass subjected to microsecond-long isotherms, we show here that temporally and spatially heterogeneous atomic-cluster activity at different length-scales drive the emergence of highly non-monotonous intensity cross-correlations. The simulated XPCS experiments reveal a variety of time-dependent intensity-cross correlations that, depending on both the structural evolution and the 𝑞-space sampling, give detailed insights into the possible structural origins of intermittent aging measured with XPCS.
Mean-field modeling and Phase-field simulation of Grain Growth under Directional driving forces
(2024)
Directional grain growth is a common phenomenon in the synthetic and natural evolution of various polycrystals. It occurs in the presence of an external driving force, such as a temperature gradient, along which grains show a preferred, yet competitive, growth. Novel additive manufacturing processes, with intense, localized energy deposition, are prominent examples of when directional grain growth can occur, beneath the melting pool. In this work, we derive a phenomenological mean-field model and perform 3D phase-field simulations to investigate the directional grain growth and its underlying physical mechanisms. The effect of the intensity of driving force is simulated and systematically analyzed at the evolving growth front as well as various cross-sections perpendicular to the direction of the driving force. We found that although the directional growth significantly deviates from normal grain growth, it is still governed by a power law relation <R> \propto t^n with an exponent n ~ 0.6–0.7. The exponent
exhibits a nontrivial dependence on the magnitude of the directional driving force, such that the lowest growth exponent is observed for intermediate driving forces. We elaborate that this can originate from the fact that the forces at grain boundary junctions evolve out of balance under the influence of the directional driving force. With increasing the driving forces, the growth exponent asymptotically approaches a value of n~0.63, imposed by the largest possible grain aspect ratio for given grain boundary energies. The current combined mean-field and phase-field framework pave the way for future exploration in broader contexts such as the evolution of complex additively manufactured microstructures.
Reliable measurement of the size of polydisperse, complex-shaped commercial nanopowders is a difficult but necessary task, e.g., for regulatory requirements and toxicity risk assessment. Suitable methods exist for the accurate characterization of the size of non-aggregated, stabilized, spherical and monodisperse nanoparticles. In contrast, industrial nanoscale powders usually require dedicated sample preparation procedures developed for the analysis method of choice. These nano-powders tend to agglomerate and/or aggregate, a behavior which in combination with an innate broad particle size distribution and irregular shape often significantly alters the achievable accuracy of the measured size parameters. The present study systematically tests two commercially available nanoscale powders using different sample preparation methods for correlative analysis by scanning electron microscopy, dynamic light scattering, Brunauer–Emmet–Teller method and differential mobility analysis. One focus was set on the sample preparation by embedding nanoparticles in carbon-based hot-mounting resin. Literature on this topic is scarce and the accuracy of the data extracted from cross sections of these particles is unclearly stated. In this paper systematic simulations on the deviation of the size parameters of well-defined series of nanoparticles with different shapes from the nominal value were carried out and the contributing factors are discussed.
AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
A density-based phase field model is developed where the free energy functional is explicitly linked with molecular dynamics and is referred to as the Molecular Phase Field Method (MoPF). MoPF simulations involve expressing interatomic potentials in terms of density to form a density based free energy functional. Inputs to this functional are taken from atomistics such that the phase field density profile matches the corresponding density profile from atomistic simulations. We analyze our results by comparing the MoPF calculated excess interfacial energies with excess interfacial energies calculated using molecular dynamics associated with several nickel grain boundaries. Additionally, a comparison is made between our results and the interfacial energies of a \Sigma7 boundary across a variety of FCC systems simulated using density functional theory. The MoPF method is able to successfully predict grain boundary free energy trends between grain boundary and material types offering an atomistically informed mesoscale formulation for studying grain boundary physics.
Knowledge representation in the Materials Science and Engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant (consistent) and variant (context-specific) knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this paper, an approach how to maintain a comprehensive MSE-centric terminology composing a mid-level ontology–the Platform MaterialDigital Core Ontology (PMDco)–via MSE community-based curation procedures is presented. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics. Additionally, it demonstrates how the PMDco lowers development and integration thresholds. Moreover, the research highlights how to fuel it with real-world data sources ranging from manually conducted experiments and simulations with continuously automated industrial applications.
This study investigates the sintering and crystallization behavior and kinetic of the bioactive glass (BG) 13–93 with nominal composition (in mol%): 54.6 SiO2 - 1.7 P2O3 - 22.1 CaO - 6.0 Na2O - 7.9 K2O - 7.7 MgO. Sintering and crystallization were investigated non-isothermally for various particle size fractions smaller than 315 μm as well as for bulk samples. Densification was not hindered by the presence of crystalline phases across all particle size fractions. Afterwards, wollastonite was found as the dominant crystal phase at higher temperature which resorb primary surface precipitation-like quartz crystallites. The growth direction shifts into volume when the sample surface is nearly covered. The crystal growth rate of wollastonite was calculated from the crystalline surface layer thickness measured during heating. The findings of this study are relevant for the high temperature processing of BG 13–93.
The unusual behavior observed in the coefficient of thermal expansion and specific heat capacity of CrFeNi, CoCrNi, and CoCrFeNi medium/high-entropy alloys is commonly referred to as the K-state effect. It is shown to be independent of the Curie temperature, as demonstrated by temperature-dependent magnetic moment measurements. CoCrFeNi alloy is chosen for detailed characterization; potential reasons for the K-state effect such as texture, recrystallization, and second-phase precipitation are ruled out. An examination of the electronic structure indicates the formation of a pseudo-gap in the Density of States, which suggests a specific chemical interaction between Ni and Cr atoms upon alloying. Hybrid Monte Carlo/Molecular Dynamic (MC/MD) simulations indicate the presence of non-negligible chemical short-range order (CSRO). Local lattice distortions are shown to be negligible, although deviations around Cr and Ni elements from those expected in a fully disordered structure are experimentally observed by X-ray absorption spectroscopy. The determined bonding distances are in good agreement with MC/MD calculations. A mechanism is proposed to explain the anomalies and calorimetric experiments and their results are used to validate the mechanism.
Irradiation assisted stress corrosion cracking (IASCC) is a form of intergranular stress corrosion cracking that occurs in irradiated austenitic alloys. It requires an irradiated microstructure along with high temperature water and stress. The process is ubiquitous in that it occurs in a wide range of austenitic alloys and water chemistries, but only when the alloy is irradiated. Despite evidence of this degradation mode that dates back to the 1960s, the mechanism by which it occurs has remained elusive. Here, using high resolution electron backscattering detection to analyze local stress-strain states, high resolution transmission electron microscopy to identify grain boundary phases at crack tips, and decoupling the roles of stress and grain boundary oxidation, we are able to unfold the complexities of the phenomenon to reveal the mechanism by which IASCC occurs. The significance of the findings impacts the mechanical integrity of core components of both current and advanced nuclear reactor designs worldwide.