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31 results for “2D material”
Data for "2D Nitrogen-Doped Graphene Materials for Noble Gas Separation"
<p>Datasets for the figures used in a manuscript accepted in Small Journal (10.1002/smll.202408525)</p> <p>Full author list:</p> <p>Veronika Šedajová#, Min-Bum Kim#, Rostislav Langer, Gobbilla Sai Kumar, Lili Liu, Zdeněk Baďura, James V. Haag, Giorgio Zoppellaro, Radek Zbořil, Praveen K Thallapally*, Kolleboyina Jayaramulu*, Michal Otyepka*</p> <p>Title: 2D Nitrogen-Doped Graphene Materials for Noble Gas Separation</p> <p>Affiliations:</p> <p>Dr. V. Šedajová, Dr. Z. Baďura, Dr. G. Zoppellaro, Prof. R. Zbořil, Prof. K. Jayaramulu and Prof. M. Otyepka<br>Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71, Olomouc, Czech Republic.<br>Email: michal.otyepka@upol.cz <br>Email: jayaramulu.kolleboyina@iitjammu.ac.in</p> <p>Dr. MB. Kim, Dr. Lili Liu, Dr. J.V. Haag, Prof. P. K Thallapally<br>Energy and Environmental Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States<br>Email: Praveen.Thallaplly@pnnl.gov</p> <p>Prof. K. Jayaramulu, Dr. G.S. Kumar<br>Hybrid Porous Materials Laboratory, Department of Chemistry, Indian Institute of Technology Jammu, Jammu and Kashmir 181221, India</p> <p>Dr. Z. Baďura, Dr. G. Zoppellaro, Prof. R. Zbořil<br>Nanotechnology Centre, CEET, VŠB-Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic.</p> <p>Prof. M. Otyepka, Dr. R. Langer<br>IT4Innovations, VŠB–Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic</p>
In Situ Exfoliation Method of Large-Area 2D Materials
<p>Datasets for In Situ Exfoliation Method of Large-Area 2D Materials publication.</p> <p>Extreme UV photoemission band mapping of 2D materials WS2, WSe2 and WTe2 exfoliated in ultra-high vacuum on Au(111), Ag(111) and Ge(001), and AgTe alloy on Ag(111). Measurements were conducted at the BALTAZAR facility in Stockholm, Sweden using a laser-based setup with arepetition rate of 250 kHz, and the fifth harmonic 18.1 eV, and at the SGM-3 beamline of the 3rd-generation ASTRID2 synchrotron radiation source in Aarhus, Denmark with a photon energy 49 eV, 53 eV and 63 eV. All measurements were performed at room temperature.<br> <br> Experimental data are provided in ibw format (Igor Pro).</p>
Probing Hyperbolic and Surface Phonon-Polaritons in 2D Materials Using Raman Spectroscopy
<p>Probing Hyperbolic and Surface Phonon-Polaritons in 2D materials using Raman Spectroscopy<br> Alaric Bergeron, Clément Gradziel, Richard Leonelli, Sébastien Francoeur<br> Published in Nat. Comm. (2023).</p> <p>https://doi.org/10.1038/s41467-023-39809-3</p> <p>Raw spectral data used for the preparation of Figures 1(b-c), 2(b-c) and 3. Please contact the corresponding author at sebastien.francoeur@polymtl.ca for additional information.<br> <br> </p>
Datesets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: 10.1002/pssa.202300148
<p>Datasets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: <a href="https://www.doi.org/10.1002/pssa.202300148">10.1002/pssa.202300148</a></p> <p>The Jupyter Notebooks for analyzing the datasets and generating all the figures are available at <a href="https://gitlab.com/JohMu/tds_hios_manuscript">https://gitlab.com/JohMu/tds_hios_manuscript</a>.</p> <p><strong>MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>192x192 scan pixels</li> <li>200x200 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 10.56 mm</li> <li>Camera pixel size: 4x5.86 µm = 23.44 µm (original dataset with 4x4 binning)</li> <li>File location: Figure 2_3_S1.zip -> 230101205338_20kV_hexz0_camz-10_posi_003_good\scan_data_bin2_centered_crop-imgNx200.h5</li> <li>The original raw dataset (23 GB, 192x192 scan pixels, 800x800 camera pixels, camera pixel size: 5.86 µm), the scan reference dataset and the Jupyter Notebook for the shift-compensation is available from the author. The dataset uploaded here is binned by a factor of 4 and shift-compensated.</li> </ul> <p><strong>C60/MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>113x113 scan pixels</li> <li>512x512 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 20.56 mm</li> <li>Camera pixel size: 5.86 µm</li> <li>File location: Figure 4.zip -> scan_data_scan113x113_gzip.h5</li> </ul> <p> </p>
Ultrahigh-throughput cross-flow filtration of solution-processed 2D materials enabled by porous ceramic membranes
Open the record for dataset details and reuse information.
Screener and Enumerator with Force-Field Optimization (SEFFO): algorithm for searching adsorption sites and configurations on 2D materials
<p>See ref:</p> <p> </p> <p>(submission stage)</p>
Logical coherence in 2D compass codes Supplementary Material
<h1>Logical coherence in 2D compass codes</h1> <p>This repo contains the tools to reproduce and share the data for the paper "Logical Coherence in 2D Compass Codes" by Balint Pato, Will Judd Staples, and Kenneth R. Brown (2025) as well as data for B. Pato, Q. Miao and K. R. Brown, "Optimal Decoding of 2D Compass Codes Under Coherent Noise," 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 448-449, doi: 10.1109/QCE60285.2024.10349.</p> <h1>Setup</h1> <p>Python 3.x is required. The instructions for a Unix/Linux system:</p> <pre><code>cd coherence-in-compass-codes-paper python3 -m venv ~/.virtualenvs/cicc . ~/.virtualenvs/cicc/bin/activate pip install -r requirements.txt -r msim/requirements.txt -r msim/requirements.dev.txt </code></pre> <p>Before running any Python file, make sure the source folder is in PYTHONPATH, for example:</p> <pre><code>export PYTHONPATH="." </code></pre> <p>A quick end-to-end test that everything works:</p> <pre><code>check/all </code></pre> <h1>Downloading data</h1> <p>Pregenerated data is available in <code>data/codes.db</code> for the Logical Coherence in 2D Compass Codes paper.</p> <p>The number of samples per code family:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>qshor[0.166667]_13x13_0</td> <td>2,178,400</td> </tr> <tr> <td>qshor[0.166667]_17x17_0</td> <td>2,185,800</td> </tr> <tr> <td>qshor[0.166667]_21x21_0</td> <td>2,210,800</td> </tr> <tr> <td>qshor[0.166667]_9x9_0</td> <td>2,176,600</td> </tr> <tr> <td>qshor[0.333333]_13x13_0</td> <td>3,310,800</td> </tr> <tr> <td>qshor[0.333333]_17x17_0</td> <td>3,317,870</td> </tr> <tr> <td>qshor[0.333333]_21x21_0</td> <td>3,344,800</td> </tr> <tr> <td>qshor[0.333333]_9x9_0</td> <td>3,308,000</td> </tr> <tr> <td>qshor[0.5]_13x13_0</td> <td>2,381,400</td> </tr> <tr> <td>qshor[0.5]_17x17_0</td> <td>2,388,000</td> </tr> <tr> <td>qshor[0.5]_21x21_0</td> <td>2,398,600</td> </tr> <tr> <td>qshor[0.5]_9x9_0</td> <td>2,379,400</td> </tr> <tr> <td>qshor[0.666667]_13x13_0</td> <td>2,808,400</td> </tr> <tr> <td>qshor[0.666667]_17x17_0</td> <td>2,827,000</td> </tr> <tr> <td>qshor[0.666667]_21x21_0</td> <td>2,876,600</td> </tr> <tr> <td>qshor[0.666667]_9x9_0</td> <td>2,805,600</td> </tr> <tr> <td>qshor[0.833333]_13x13_0</td> <td>3,869,600</td> </tr> <tr> <td>qshor[0.833333]_17x17_0</td> <td>3,883,200</td> </tr> <tr> <td>qshor[0.833333]_21x21_0</td> <td>3,913,400</td> </tr> <tr> <td>qshor[0.833333]_9x9_0</td> <td>3,861,400</td> </tr> <tr> <td>surface_13x13</td> <td>2,004,800</td> </tr> <tr> <td>surface_17x17</td> <td>2,004,800</td> </tr> <tr> <td>surface_21x21</td> <td>2,004,800</td> </tr> <tr> <td>surface_9x9</td> <td>2,004,800</td> </tr> <tr> <td>zstackedshor[h11]_11x11_0</td> <td>612,250</td> </tr> <tr> <td>zstackedshor[h1]_11x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_5x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_7x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_9x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h2]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h5]_5x5_0</td> <td>700,000</td> </tr> <tr> <td>zstackedshor[h7]_7x7_0</td> <td>700,000</td> </tr> </tbody> </table> <p>For the surface code ML database:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>surface_101x101</td> <td>40,000</td> </tr> <tr> <td>surface_13x13</td> <td>1,599,200</td> </tr> <tr> <td>surface_151x151</td> <td>40,000</td> </tr> <tr> <td>surface_17x17</td> <td>1,599,200</td> </tr> <tr> <td>surface_201x201</td> <td>40,000</td> </tr> <tr> <td>surface_21x21</td> <td>1,599,200</td> </tr> <tr> <td>surface_23x23</td> <td>1,600,000</td> </tr> <tr> <td>surface_251x251</td> <td>40,000</td> </tr> <tr> <td>surface_25x25</td> <td>1,600,000</td> </tr> <tr> <td>surface_27x27</td> <td>1,600,000</td> </tr> <tr> <td>surface_29x29</td> <td>1,600,000</td> </tr> <tr> <td>surface_33x33</td> <td>1,600,000</td> </tr> <tr> <td>surface_37x37</td> <td>1,600,000</td> </tr> <tr> <td>surface_45x45</td> <td>2,000,000</td> </tr> <tr> <td>surface_53x53</td> <td>2,000,000</td> </tr> <tr> <td>surface_61x61</td> <td>2,000,000</td> </tr> <tr> <td>surface_69x69</td> <td>2,000,000</td> </tr> <tr> <td>surface_9x9</td> <td>1,599,380</td> </tr> </tbody> </table> <p>Sample queries:</p> <ul> <li>to find the number of samples above:</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>order</span> <span>by</span> theta_phys <span>asc</span>; </code></pre> <ul> <li>to find the number of samples per datapoint for surface codes</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>having</span> code_id <span>like</span> <span>'surface%'</span> <span>order</span> <span>by</span> theta_phys <span>asc</span> ; </code></pre> <ul> <li>to query data underlying the plots for infidelity metrics for the d=9 qshor=5/6 compass codes:</li> </ul> <pre><code><span>select</span> <span>*</span> <span>from</span> qshor_loaf <span>where</span> code_id <span>like</span> <span>'qshor%0.83%9x9%'</span>; </code></pre> <h2>Database schema</h2> <pre><code><span>-- code families are the roots of parametrized code hierarchies, e.g. surface, qshor. The convention is to also use the code_family as table names describing the parametrized members. </span> <span>CREATE</span> <span>TABLE</span> CODE_FAMILIES(CODE_FAMILY, <span>UNIQUE</span>(CODE_FAMILY)); <span>-- surface code members. Technically possible to create non-square surface codes, but in this paper we only explored square ones. </span> <span>CREATE</span> <span>TABLE</span> SURFACE(CODE_ID, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>-- families of qshor (x check density) parametrized random compass codes. </span> <span>CREATE</span> <span>TABLE</span> qshor(CODE_ID, QSHOR, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>--the main samples table, for a given code, and physical rotation angle the logical rotation angle is recorded</span> <span>CREATE</span> <span>TABLE</span> LOGICAL_ANGLE_SAMPLES(CODE_ID,THETA_PHYS TEXT, THETA_LOGICAL TEXT); <span>-- diamond distance metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_loaf(code_id, theta_phys, mean, std, num_records); <span>-- diamond distance metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor(CODE_ID, ZSHOR_HEIGHT, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>-- ML decoder version </span> <span>CREATE</span> <span>TABLE</span> qshor_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> qshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_loaf_ml(code_id, theta_phys, mean, std, num_records); </code></pre> <h1>Plots</h1> <p>Make sure that you have the data under <code>data/codes.db</code> - this needs to be a SQLite database. See the previous section on how to create it from the published data.</p> <p>Plots are generated under the folder <code>figures</code>. There are separate entry points for each figure in the paper:</p> <ul> <li>combined thresholds in the appendix resulting in <code>figures/threshold_plot-combined_full.pdf</code> and <code>figures/threshold_plot-combined_zoom.pdf</code>:</li> </ul> <pre><code>python cicc/plot/combined_threshold.py </code></pre> <ul> <li>the main plot containing the manually extracted thresholds for the random compass codes and the surface code:</li> </ul> <pre><code>python cicc/plot/qshor_family.py </code></pre> <ul> <li>the QCE2024 poster / extended abstract plots</li> </ul> <pre><code>python cicc/plot/qce2024_figures.py </code></pre> <ul> <li>the repetition code and Z-stacked Shor code plots matching the formulae plots</li> </ul> <pre><code>python cicc/plot/zstack_zshor_thresholds.py </code></pre> <h1>A note on angle conventions</h1> <p>All rotations in this project are around the Z-axis. There are two ways one can interpret the rotation angle based on the unitary rotation:</p> <ul> <li>spin angles: Rz(theta_spin) = exp(-i theta_spin/2 Z)</li> <li>direct angles: Rz(theta_direct) = exp(i theta_direct Z)</li> </ul> <p>Thus, for the same rotation unitary, theta_spin = - 2 * theta_direct. The paper by Bravyi, Engelbrecht, Konig and Peard [^bravyi2018] for the rotated surface code uses the direct angles convention, and similarly, msim uses direct angles. However, this paper uses spin angles closer to the physics convention.</p> <p>[^bravyi2018]:Bravyi, Sergey, Matthias Englbrecht, Robert König, and Nolan Peard, ‘Correcting Coherent Errors with Surface Codes’, Npj Quantum Information, 4.1 (2018), 55 <a href="https://doi.org/10.1038/s41534-018-0106-y">https://doi.org/10.1038/s41534-018-0106-y</a></p> <p>The table below summarizes the angle conventions in different parts of the codebase to avoid confusion:</p> <table> <tbody><tr> <th>Place</th> <th>convention</th> </tr> </tbody><tbody> <tr> <td>physical angles for the samplers in this project</td> <td>direct angles / pi</td> </tr> <tr> <td>msim simulator</td> <td>direct angles</td> </tr> <tr> <td>msim coeffs framework</td> <td>spin angles</td> </tr> <tr> <td>database logical angles</td> <td>direct angles</td> </tr> <tr> <td>database physical angles</td> <td>direct angles</td> </tr> <tr> <td>plots</td> <td>spin angles / pi</td> </tr> </tbody> </table> <h1>Generating your own data</h1> <h2>Random Compass Codes</h2> <p>Generating data for random compass codes for a given set of qshor values, distances and physical theta values:</p> <pre><code> python cicc/sample_angles/qshor_angles.py --help usage: qshor_angles.py [-h] [--db DB] --dz DZ --q Q --theta_phys THETA_PHYS [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --q Q Qshor probability metric --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples, which is divided across sqrt(num_runs) randomly generated codes </code></pre> <p>For example:</p> <pre><code>python cicc/sample_angles/qshor_angles.py --dz "[9, 13, 17, 21]" --theta_phys="np.linspace(0.01,0.25, 10)" --qshor="[1/6, 2/6, 3/6, 4/6, 5/6]" </code></pre> <h2>Surface code</h2> <p>Generating data for the rotated surface code:</p> <pre><code>python cicc/sample_angles/rsc_angles.py --help usage: rsc_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS] [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --batch_size BATCH_SIZE batch size for writing to the db --num_workers NUM_WORKERS parallelism --num_runs NUM_RUNS number of iterations: the total number of samples </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/rsc_angles.py --dz "[5, 9, 13, 17, 21, 25, 29]" --theta_phys="np.linspace(0.01,0.25, 10)" </code></pre> <h2>Repetition codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: repcodes_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --dz "[5,7,9,11]" --theta_phys "list(reversed(list(np.linspace(0.04,0.18,20))))" --num_runs 10000 </code></pre> <h2>Z-stacked Shor codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: zstacked_shor_angles.py [-h] [--db DB] --dz DZ [--height HEIGHT] [--zshor ZSHOR] --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] Sample angles for ZStackedShor codes. Example usage: Run 10,000 samples for height-2 and height-3 ZStackedShor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height [2,3] --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for Z-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --zshor --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for X-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height 1 --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --height HEIGHT height of the Z-Shor code block --zshor ZSHOR Z-shor code - Z-Shor height equals to dz --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <h2>Regenerating aggregated statistics</h2> <p>In the combined plot (<code>cicc/plot/combined_threshold.py</code>), by default, the already computed statistics are plotted. Hence, when new data is generated, these need to be recalculated. In <code>cicc/plot/combined_threshold.py</code> there is a section that can be modified to trigger the required recalculation (slow):</p> <pre><code> recompute( conn, <span># set to True to recompute qshor statistics </span> recompute_qshor=<span>False</span>, <span># set to True to recompute surface code statistics </span> recompute_rsc=<span>False</span>, <span># set to True to recompute qshor statistics with ML decoding </span> recompute_ml_qshor=<span>False</span>, <span># set to True to recompute surface code statistics with ML decoding</span> recompute_ml_rsc=<span>False</span>, )</code></pre>
Advanced Materials - Epitaxial Growth of Large-Scale 2D CrTe2 Films on Amorphous Silicon Wafers With Low Thermal Budget
<p>2D van der Waals (vdW) magnets open landmark horizons in the development of innovative spintronic device architectures. However, their fabrication with large scale poses challenges due to high synthesis temperatures (>500 °C) and difficulties in integrating them with standard complementary metal-oxide semiconductor (CMOS) technology on amorphous substrates such as silicon oxide (SiO<sub>2</sub>) and silicon nitride (SiN<em><sub>x</sub></em>). Here, a seeded growth technique for crystallizing CrTe<sub>2</sub> films on amorphous SiN<em><sub>x</sub></em>/Si and SiO<sub>2</sub>/Si substrates with a low thermal budget is presented. This fabrication process optimizes large-scale, granular atomic layers on amorphous substrates, yielding a substantial coercivity of 11.5 kilo-oersted, attributed to weak intergranular exchange coupling. Field-driven Néel-type stripe domain dynamics explain the amplified coercivity. Moreover, the granular CrTe<sub>2</sub> devices on Si wafers display significantly enhanced magnetoresistance, more than doubling that of single-crystalline counterparts. Current-assisted magnetization switching, enabled by a substantial spin–orbit torque with a large spin Hall angle (85) and spin Hall conductivity (1.02 ×  10<sup>7</sup> ℏ/2e  Ω⁻¹  m⁻¹), is also demonstrated. These observations underscore the proficiency in manipulating crystallinity within integrated 2D magnetic films on Si wafers, paving the way for large-scale batch manufacturing of practical magnetoelectronic and spintronic devices, heralding a new era of technological innovation.</p>
Reconstructing the 2D Material Structure from Diffraction Pattern with Physical-Sensitive Deep Learning
<p>Dataset, saved Models' parameters, and test set</p>
Finite Element Simulations of 2D Bravais lattice family within photonic crystals fabricated on a photopolymer material via three-beam holography
<p>Videos illustrating Finite Element Simulations of 2D Bravais lattice families within photonic crystals fabricated on a photopolymer material through three-beam holography. The two-way diffusion model is used to simulate the photopolymerization process during the recording process and the formation of the diffraction grating. The simulation results are under a CC license. <br><br>Maged Shaban</p>
Data and code for: Ultrafast switching of trions in 2D materials by terahertz photons
<p>Data for each individual figure panel and used codes </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.