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42 results for “grain boundary”
Grain Boundary Engineering Enhances the Thermoelectric Properties of Y2Te3
<div> <p>The performance of thermoelectric materials is typically assessed using the dimensionless figure of merit, <em>zT</em>. Increasing <em>zT</em> is challenging due to the intricate relationships between electrical and thermal transport properties. This study focuses on Y<sub>2</sub>Te<sub>3</sub>-based thermoelectric materials, which are predicted to be promising for high-temperature applications due to their inherently low lattice thermal conductivity. A series of Y<sub>2+x</sub>Te<sub>3</sub> compositions with excess Y was synthesized to explore the effects on electronic and structural characteristics. Density functional theory calculations suggest that additional Y atoms increase charge carriers, thereby enhancing electrical conductivity and boosting thermoelectric performance. X-ray diffraction analysis reveals that the presence of excess Y reduces lattice volume and alters bonding structures. Furthermore, the addition of Bi significantly enhances the power factor by promoting the segregation of elemental Bi particles and the formation of Y-Bi-rich grain boundaries, which improve weighted mobility. This microstructural optimization leads to a fourfold increase in the Seebeck coefficient, resulting in a peak <em>zT</em> of 1.23 at 973 K and a predicted maximum conversion efficiency of 10.3% under a temperature difference of 673 K. These findings highlight the potential of Y<sub>2</sub>Te<sub>3</sub> for high-temperature thermoelectric applications and demonstrate the effectiveness of grain boundary engineering in enhancing thermoelectric performance.</p> <p> </p> <p>DOI: <a href="https://doi.org/10.1002/aenm.202404243">10.1002/aenm.202404243</a></p> </div>
Multiscale simulation data for demonstrating the analogy between grain boundaries and Brownian ratchets
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Data from: Ab initio grand canonical Monte Carlo calculation of grain boundary composition and structure
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Investigation of slip transfer across HCP grain boundaries with application to cold dwell facet fatigue
<p>Data for "Investigation of slip transfer across HCP grain boundaries with application to cold dwell facet fatigue"<br> http://dx.doi.org/10.1016/j.actamat.2017.01.021</p> <p>This Data folder contains 4 data files:<br> (1) Data_for_Figure_3.xlsx<br> (2) Data_for_Figure_4.xlsx<br> (3) Data_for_Figure_9.xlsx<br> (4) Data_for_Figure_13.xlsx</p> <p>-<br> If readers need further information, please feel free to contact:<br> zebang.zheng12@imperial.ac.u</p>
Dataset for the article entitled: "Revealing the strengthening contribution of stacking faults, dislocations and grain boundaries in severely deformed LPBF AlSi10Mg alloy"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>AlSi10Mg_HT320.ang - Heat treated sample</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>TKD data:</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>XRD data:</p> <p>AlSi10Mg_HT320.ASC - Heat treated sample</p> <p>AlSi10Mg_HT320_ECAP100.ASC - Heat treated ECAP processed sample</p>
Slip systems activity and grain boundary sliding investigation in zinc alloy
<p>Data obtained during the investigation of slip system activity and grain boundary sliding in Zn-0.5Cu alloy.</p> <p>Dataset contains RAW and processed EBSD data, SEM images, AFM data, and data analysis results in a form of Origin project data file.</p>
Data for paper: Atomistic simulations of grain boundary migration under recrystallisation conditions
<p>The archive contains the data used to produce the results for the paper: Atomistic simulations of grain boundary migration under recrystallisation conditions, by C.P.Race, submitted to the IOP journal Modelling and Simulation in Materials Science and Engineering</p> <p>The archive contains:</p> <p>1) Juypter Notebooks that load and analyse the data, producing the figures used in the publication<br> 2) An author pre-print of the paper, prior to amendments suggested by the referees<br> 3) Copies of the figures produced by the notebooks<br> 4) .txt files of the data used in the publication.<br> 5) A README file describing the contents</p>
Data Supporting Defect segregation facilitates oxygen transport at fluorite UO2 grain boundaries
<p>Data support article accepted for publication in Philosophical Transactions A. Data includes inputs and outputs for molecular dynamics simulations. Where impractical simulations can be rerun from the inputs provided.</p>
XYZ file of monolayer MoS2 with 5- and 7-membered rings grain boundary
<p>Structure files of monolayer MoS2 with 5- and 7-membered rings grain boundary. <br>mos2_500x500_GB.xyz: sample size is 500 Å x 500 Å<br>mos2_234atoms_GB.xyz: consisting of 234 atoms</p>
Grain boundaries are Brownian ratchets
<p># Phase-field crystal simulations for paper "Grain Boundaries are Brownian Ratchets"</p> <p>Openly available Matlab simulation files used to produce the phase-field crystal results from https://www.science.org/doi/10.1126/science.adp1516 see also https://zenodo.org/records/13319941</p> <p>Runable Matlab files can be found within the respective folders BICRYSTAL, POLYCRYSTAL_MAINTEXT and POLYCRYSTAL_LARGER_ACCELERATION and are named by the corresponding figures.</p> <p>## Update<br>Since the acceptance and publication of our paper, many friends and colleagues have been interested in whether a larger acceleration in grain growth rate through cyclic annealing can be achieved. We updated the dataset by adding a new version with an additional set of cyclic annealing simulations, indicating even more robust evidence of cyclic annealing-accelerating grain growth. The additional code can be found in the folder POLYCRYSTAL_LARGER_ACCELERATION, and the details and discussion can be found in the README file in the same folder.</p> <p>## Other simulations<br>The remaining input files for the MD simulations, neb calculations and python codes for the Markov modellings are not included in this repository and can instead be found under https://doi.org/10.5061/dryad.0k6djhb8v</p> <p><br>## Disclaimer<br>The software is released here under the MIT license. We kindly ask to refer to/cite the related publication https://www.science.org/doi/10.1126/science.adp1516 for any usage and extension. The authors are thankful for any advice considering typos, mistakes, and/or discussions around the code/implementation or the topic of the related publication in general. Please do not hesitate to contact one of the authors, Maik Punke, via: maik.punke@tu-dresden.de</p>
Data for "Topological grain boundary segregation transitions"
<p>Cite as: Vivek Devulapalli et al. ,Topological grain boundary segregation transitions.Science386,420-424(2024). DOI:10.1126/science.adq4147<br><br>This repository contains the raw data from STEM imaging, EDS, and EELS experiments, the code used for GB simulations and theoretical calculations presented in the paper. </p> <p>=========================================================</p> <p>MDMC-SGC directory contains the MD/MC simulation in the semi-grand-canonical<br>ensemble (Fig. 4 of the paper).</p> <p><br>Fe-Ti-phase-diagram<br>===================</p> <p>First, the bulk concentration of Fe in Ti is calculated as a function<br>of the chemical potential difference Δµ between Fe and Ti. This is<br>required to calculate the grain boundary excess over the bulk.</p> <p>Here, it turns out that the bulk concentration is approximately zero<br>in the range of Δµ investigated.</p> <p><br>MD/MC simulations of grain boundaries<br>=====================================</p> <p>The following sample names map to the naming in the paper:</p> <p>* ABC: Ti ground state structure<br>* large-1cage-2300000: isolated cage<br>* larger-2cages-3200000: double cage<br>* large-02-10000220: one layer of cages<br>* large-01-10000367: second layer of cages forming</p> <p>Each directory contains subdirectories for all investigated Δµ. The<br>subdirectory `final-states` contains the final snapshots for each Δµ.</p> <p>The script `prepare.py` was used to set up the simulations (template<br>for the LAMMPS input file is `lmp.in.template`). The script<br>`collect.py` was used to extract the thermodynamic excess properties<br>of the grain boundaries, stored in the file `T_0300K.excess.dat` in<br>each subdirectory.</p> <p>The notebook `plot-excess.ipynb` can be used to plot the excess data.</p> <p>=========================================================</p> <p># GRand canonical Interface Predictor (GRIP)</p> <p>_Authors: [Enze Chen](https://enze-chen.github.io/) (Stanford University) and<br>[Timofey Frolov](https://people.llnl.gov/frolov2) (Lawrence Livermore National Laboratory)_ <br>_Version: 0.1.2024.01.21_</p> <p>An algorithm for performing grand canonical optimization (GCO) of interfacial<br>structure (e.g., grain boundaries) in crystalline materials.<br>It automates sampling of slab translations and reconstructions<br>along with vacancy generation and finite temperature molecular dynamics (MD).<br>The algorithm repeatedly samples different structures in two phases:<br> 1. Structure generation and manipulation is largely handled using the<br> [Atomic Simulation Environment (ASE)](https://wiki.fysik.dtu.dk/ase/).<br> 2. Molecular dynamics and static relaxations are currently performed using<br> [LAMMPS](https://www.lammps.org), although in principle other energy<br> evaluation methods (e.g., density functional theory in [VASP](https://www.vasp.at))<br> may be used.</p> <p>------</p> <p>## Dependencies<br>- [Python](https://www.python.org/) (3.6+)<br>- [NumPy](https://numpy.org/) (1.23.0)<br>- [ASE](https://wiki.fysik.dtu.dk/ase/) (3.22.1)<br>- [LAMMPS](https://www.lammps.org) (stable)</p> <p>_Optional_<br>- [pandas](https://pandas.pydata.org/) (1.5.3)<br>- [Matplotlib](https://matplotlib.org/stable/index.html) (3.5.3)</p> <p><br>## Usage</p> <p>Assuming the above libraries are installed, clone the repo and make the <br>appropriate modifications in `params.yaml` (see file for detailed comments), <br>including the path to the LAMMPS binary on your system.<br>If you wish, you can supply your own slabs for the bicrystal configuration as<br>POSCAR_LOWER and POSCAR_UPPER (in the [POSCAR](https://www.vasp.at/wiki/index.php/POSCAR)<br>file format).<br>Then call:<br>```python<br>python main.py<br>```<br>If you don't have LAMMPS or just want to test the script, you can run it with the `-d` flag.<br>See the `.examples` folder for a SLURM submission script for parallel execution (preferred).</p> <p><br>## File structure<br>- `main.py`: Script to launch everything.<br>- `params.yaml`: Simulation parameters; **you'll want to edit this.**<br>- `core`: Main classes (`Bicrystal`, `Simulation`, etc.)<br>- `utility`: Main helper functions (`utils.py`, `unique.py`, etc.)<br>- `simul_files`: Files for simulations (LAMMPS input files, etc.)<br>- `best`: All relaxed structures are stored here. The naming convention is:<br>`lammps_Egb_n_X-SHIFT_Y-SHIFT_X-REPS_Y-REPS_TEMP_STEPS`</p> <p><br>Duplicate files are periodically deleted by calling `clear_best()` in `utils/unique.py`.<br>The default method cleans about 1-3% of files on average.<br>Use the `-e` flag for more aggressive cleaning (>50%).<br>Use the `-s` flag to save the processed results to CSV from a pandas DataFrame.</p> <p>Results can be visualized by running `utils/plot_gco.py` and it generates a GCO plot<br>of $E_{\mathrm{gb}}$ vs. $n$.<br>The `.examples` folder has this plot for several boundaries.<br>By default executing this file will save both the results (CSV) and the figure (PNG) <br>to the same folder as the GRIP output files.</p> <p><br>## Citation<br>If you use GRIP in your work, we would appreciate a citation to the original manuscript:</p> <p>> Enze Chen, Tae Wook Heo, Brandon C. Wood, Mark Asta, and Timofey Frolov.<br>"Grand canonically optimized grain boundary phases in hexagonal close-packed titanium."<br>_arXiv:XXXX.YYYYY [cond-mat.mtrl-sci]_, 2024.</p> <p>or in BibTeX format:</p> <p>```<br>@article{chen_2024_grip,<br> author = {Chen, Enze and Heo, Tae Wook and Wood, Brandon C. and Asta, Mark and Frolov, Timofey},<br> title = {Grand canonically optimized grain boundary phases in hexagonal close-packed titanium},<br> year = {2024},<br> journal = {arXiv:XXXX.YYYYY [cond-mat.mtrl-sci]},<br> doi = {10.48550/arXiv.XXXX.YYYYY},<br>}<br>```</p> <p>=========================================================</p> <p> </p>
graphene grain boundary superlattice samples
<p>Samples (atom coordinates in .xyz format) for graphene grain boundary superlattices studied in the following paper:</p> <p>Haikuan Dong, Yuqi Liu, Zihan Tan, Qing Li, Xiaoye Zhou, Shujun Zhou, Xiaoming Xiu, Coherent heat transport in graphene grain boundary superlattices.</p>
graphene/hexagonal-BN grain boundary samples
<p>Samples (atom coordinates in .xyz format) for graphene/hexagonal-BN grain boundaries studied in the following paper:</p> <p>Haikuan Dong, Petri Hirvonen, Zheyong Fan, Ping Qian, Yanjing Su, and Tapio Ala-Nissila, Heat transport across graphene/hexagonal-BN heterostructures from phase-field crystal model and molecular dynamics simulations, submitted.</p>
Grain Boundary Motion in eGaIn-Zinc Embrittlement
<p>Timelapse of grain boundary motion in a region of a zinc specimen in which the grain boundaries have been wetted by eutectic gallium indium (eGaIn). The dark gray phase is the solid zinc crystals, bright phase is indium precipitates formed by indium being rejected from the liquid phase, and the intermediate phase is molten eGaIn filling the prior grain boundaries. </p>
eGaIn Propagating Through the Grain Boundaries in a Zn Foil
<p>Propagation of liquid eutectic gallium indium (bright phase) through the grain boundaries of a zinc (dark phase) foil. The propagation direction is right to left. The initial exposure site is on the right edge of the frame. </p>
The influences of progenitor filtering, domestication selection and the boundaries of nature on the domestication of grain crops
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Data for grain boundaries are not the source of Urbach tails in the CIGSe
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Dataset - Learning Grain Boundary Segregation Energy Spectra in Polycrystals
<p>Accompanying Dataset for the article "Learning Grain Boundary Segregation Energy Spectra in Polycrystals". The dataset contains 1) an example Jupyter Notebook with all necessary code to train and use the machine learning models outlined in the paper, and 2) a database of segregation spectra of 250+ binary alloys, in the form of LAMMPS text dump files of solvent polycrystals with predicted grain boundary solute segregation energies. Please refer to the README.pdf for detailed file description.</p>
Micro-Laue diffraction data used in the publication 'Dislocation density distribution at slip band-grain boundary intersections'
<p>The Ti-Bi.xml file is an indexed data set from a differential aperture X-ray micro-Laue diffraction experiment at beamline 34-ID-E, Advanced Photon Source, USA. To obtain strain and rotation field the data need to be feed into LaueGo as specified in <a href="https://www.aps.anl.gov/Science/Scientific-Software/LaueGo">https://www.aps.anl.gov/Science/Scientific-Software/LaueGo</a></p> <p>The GND_data_Zenodo.mat is a Matlab file containing the rotation fields and GND density results. The data structure is explained below:</p> <p>GND_rho_2D and GND_rho_3D are the density of the 33 types of dislocations for all 31 slices of data. Each slice is a 4462x33 matrix where the columns are 33 types of dislocations specified in 'sliplabels'. Each columns has 4462 points giving rise to the 194x23 pixel size image of each slice of data.</p> <p>GND_total_2D and GND_total_3D are the total dislocation density maps in 2D and 3D. Each cell is a slice of the 3D data with 194x23 pixels.</p> <p>For more information, please contact Dr Ben Britton: b.britton@imperial.ac.uk</p>
Analysis of slip transfer across grain boundaries in Ti via DCT and HRDIC [Dataset]
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