Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMaster sintering curve with dissimilar grain growth trajectories: A case study on MgAl2O4
113
0
0.0
(
0
)
Authors
Gabriel Kerbart
Ask ChatGPT about the research
No Arabic abstract
Sintering is a key step in the processing of high performance ceramics. Both the density and the grain size play a crucial role on the ceramic sintering kinetics and the final material properties. The master sintering curve (MSC) is a well-known tool for exploring sintering models kinetics. However, the conventional MSC theory assumes a unique sintering trajectory, while our study on MgAl2O4 spinel shows dissimilar growth response. Parks MSC theory has been applied and compared with the conventional MSC approach for obtaining the activation energy with and without dissimilar grain growth trajectories.
rate research
Read More
Volume shrinkage, grain growth, and their interaction are major events occurring during free sintering of ceramics. A high temperature sintering dilatometry curve is influenced by these both phenomena. It is shown that the continuum theory of sintering can be utilized in the format enabling the extraction of the maximum amount of information on the densification and grain growth kinetics based on a simple dilatometry test. We present here the capability of such a fast approach (Dilatometry based Grain growth Assessment DGA) utilized for the modeling of sintering and grain growth of zirconia.
Grain boundary migration is driven by the boundarys curvature and external loads such as temperature and stress. In intercalation electrodes an additional driving force results from Li-diffusion. That is, Li-intercalation induces volume expansion of the host-electrode, which is stored as elastic energy in the system. This stored energy is hypothesized as an additional driving force for grain boundaries and edge dislocations. Here, we apply the 2D Cahn-Hilliard$-$phase-field-crystal (CH-PFC) model to investigate the coupled interactions between highly mobile Li-ions and host-electrode lattice structure, during an electrochemical cycle. We use a polycrystalline FePO$_{4}$/ LiFePO$_{4}$ electrode particle as a model system. We compute grain growth in the FePO$_{4}$ electrode in two parallel studies: In the first study, we electrochemically cycle the electrode and calculate Li-diffusion assisted grain growth. In the second study, we do not cycle the electrode and calculate the curvature-driven grain growth. External loads, such as temperature and stress, did not differ across studies. We find the mean grain-size increases by $sim11%$ in the electrochemically cycled electrode particle. By contrast, in the absence of electrochemical cycling, we find the mean grain-size increases by $sim2%$ in the electrode particle. These CH-PFC computations suggest that Li-intercalation accelerates grain-boundary migration in the host-electrode particle. The CH-PFC simulations provide atomistic insights on diffusion-induced grain-boundary migration, edge dislocation movement and triple-junction drag-effect in the host-electrode microstructure.
Grain boundaries (GBs), an important constituent of polycrystalline materials, have a wide range of manifestion and significantly affect the properties of materials. Fully understanding the effects of GBs is stalemated due to lack of complete knowledge of their structures and energetics. Here, for the first time, by taking graphene as an example, we propose an analytical energy functional of GBs in angle space. We find that an arbitrary GB can be characterized by a geometric combination of symmetric GBs that follow the principle of uniform distribution of their dislocation cores in straight lines. Furthermore, we determine the elusive kinetic effects on GBs from the difference between experimental statistics and energy-dependent thermodynamic effects. This study not only presents an analytical energy functional of GBs which could also be extended to other two-dimensional materials, but also sheds light on understanding the kinetic effects of GBs in material synthesizing processes.
Single crystal metal films on insulating substrates are attractive for microelectronics and other applications, but they are difficult to achieve on macroscopic length scales. The conventional approach to obtaining such films is epitaxial growth at high temperature using slow deposition in ultrahigh vacuum conditions. Here we describe a different approach: sputter deposition at modest temperatures followed by annealing to induce secondary grain growth. We show that polycrystalline as-deposited Cu on alpha-Al2O3(0001) can be transformed into Cu(111) with centimeter-sized grains. Employing optical microscopy, x-ray diffraction, and electron backscatter diffraction to characterize the films before and after annealing, we find a particular as-deposited grain structure that promotes the growth of giant grains upon annealing. To demonstrate one potential application of such films, we grow graphene by chemical vapor deposition on wafers of annealed Cu and obtain epitaxial graphene grains of 0.2 mm diameter.
We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach and show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
