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252 results for “Atomic data”
Research data supporting "Atomic-Scale Patterning of Arsenic in Silicon by Scanning Tunneling Microscopy"
<p>Research data supporting the publication: Stock, T. J. Z, <em>et. al.</em>, <strong>2020</strong>, Atomic-Scale Patterning of Arsenic in Silicon by Scanning Tunneling Microscopy, <em>ACS Nano</em>, https://dx.doi.org/10.1021/acsnano.9b08943</p>
Data-driven Household Load Flexibility Modelling: Shiftable Atomic Load
<p>This is flexibility model for shiftable atomic loads (i.e. washing machine, dryers and dish washers). The model is based on real 1-minute level measurements from real households over period of time. The details of the model are described in [R]. The model is implemented in Excel for cloth washing machines weekday consumption and flexibility scenario and all the required data is included for modelling the other equipment.</p> <p>[R] Degefa, M.Z., Sæle, H., Petersen, I. and Ahcin, P., 2018, October. Data-driven Household Load Flexibility Modelling: Shiftable Atomic Load. In <em>2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)</em> (pp. 1-6). IEEE.</p> <p><a href="https://ieeexplore.ieee.org/document/8571836">https://ieeexplore.ieee.org/document/8571836</a></p>
Extended Data: Structure-dependence of the atomic-scale mechanisms of Pt electrooxidation and dissolution
<p><strong>Abstract:</strong></p> <p>Platinum dissolution and restructuring due to surface oxidation are primary degradation mechanisms that limit the lifetime of Pt-based electrocatalysts for electrochemical energy conversion. Here, we studied well-defined Pt(100) and Pt(111) electrode surfaces by in situ high-energy surface X-ray diffraction, on-line inductively coupled plasma mass spectrometry, and density functional theory calculations, to elucidate the atomic-scale mechanisms of these processes. The locations of the extracted Pt atoms after Pt(100) oxidation reveal distinct differences from the Pt(111) case, which explains the different surface stability. The evolution of a specific stripe oxide structure on Pt(100) produces unstable surface atoms which are prone to dissolution and restructuring, leading to one order of magnitude higher dissolution rates.</p> <p><strong>Contents of this repository:</strong></p> <p><strong>1. SXRD data:</strong></p> <p>The experiments for the acquisition of the raw SXRD data were performed at the the European Synchrotron Radiation Facility, Grenoble, France at the beamlines ID31 and ID03. We thank H. Isern and T. Dufrane for the help during the SXRD experiments.</p> <ul> <li>tomo_tomo.spec is the file with the X-ray diffraction metadata for the CTR scans. It is a plain text file.</li> <li>Each HESXRD dataset is saved in a folder, which denotes the potential (e.g. 1V0.zip). The png-image files are previews of the corresponding dataset. The individual raw cbf files can be opened by pyMCA or silx. From python, the images can be accessed using the fabio library.</li> <li>calibration.zip contains the pyFAI calibration files and a list with indexed Bragg reflections for the UB matrix calculation</li> <li>CTRs_parameters.zip contains the averaged CTR structure factors used for the structual analysis as well as files with the atomic coordinates of the refined structural model.</li> <li>steps.zip contains the full datasets from the potential step experiments in Fig. 1e.</li> </ul> <p><strong>2. DFT:</strong></p> <ul> <li>CONTCARs.zip contains the atomic coordinates of the optimized computational models.</li> </ul> <p> </p> <p> </p>
Source Data - Ilzhofer et al. - Phase coherence in out-of-equilibrium supersolid states of ultracold dipolar atoms
<p>Source data for following publication:</p> <p>"Phase coherence in out-of-equilibrium supersolid states of ultracold dipolar atoms" (2019)</p> <p>P. Ilzhofer and M. Sohmen and G. Durastante and Claudia Politi and A. Trautmann and G. Morpurgo and T. Giamarchi and L. Chomaz and M. Mark and F. Ferlaino</p> <p>Institut f ̈ur Quantenoptik und Quanteninformation, ̈Osterreichische Akademie der Wissenschaften, 6020 Innsbruck, Austria</p> <p>Institut f ̈ur Experimentalphysik und Zentrum f ̈ur Quantenoptik,Universit ̈at Innsbruck, Technikerstraße 25, 6020 Innsbruck, Austria</p> <p>DQMP, University of Geneva, 24 Quai Ernest-Ansermet, CH-1211 Geneva, Switzerland</p>
Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel - data
<p>Data for 'Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel' paper (<a href="https://doi.org/10.1016/j.mtla.2020.100946">https://doi.org/10.1016/j.mtla.2020.100946</a>) </p>
Data presented in "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states"
<p>These are the data presented in our paper "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states" which has been accepted for publication in the New Journal of Physics (as of 07 November 2016).</p>
Data for Properties of Kinetic Transition Networks for Atomic Clusters and Glassy Solids
<p>Databases of minima and transition states for Morse clusters, in two and three dimensions at a variety of ranges.</p>
Transferability of atomic energies from alchemical decomposition - additional data
<p>Additional data to the paper "Transferability of atomic energies from alchemical decomposition"</p><p><a href="https://doi.org/10.48550/arXiv.2311.04784">https://doi.org/10.48550/arXiv.2311.04784</a></p><p>Atomic energies of selected QM9 compounds calculated with different decomposition schemes (alchemy, IQA and IBO/IAO).</p><p>Code to generate the input-file and the rescaled pseudopotential files for DFT calculations with fractional core charges with the CPMD program and to calculate alchemical atomic energies from the CPMD output.</p><p> </p>
Data Repository for Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr
<p><span>Data repository for: Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr</span></p> <p><span><span>This data repository contains the raw data as measured on the experimental setup, simulations, analysis scripts and plotting scripts to reproduce the plots shown in the manuscript’s figures.</span></span></p> <p><span><span>Code for plotting: Matlab R2021b<br>The raw data is either stored as MatLab structs (.mat) or accessible through the .json files.</span></span></p> <p><span><span>See ReadMe.txt for more information.</span></span></p>
Supplemental data and figures behind: Atom scale element and isotopic investigation of 25Mg-rich stardust from a H-burning supernova
<p>We have discovered a presolar olivine from ALH 77307 with the highest <sup>25</sup>Mg isotopic composition measured in a silicate to date (δ<sup>25</sup>Mg = 3025.1‰ ± 38.3‰).<strong> </strong>Its isotopic compositions challenge current stellar models, with modelling of Mg, Si and O showing a closest match to formation in a supernova where hydrogen ingestion occurred in the pre-supernova phase. Presolar grains within primitive astromaterials retain records of processes and environmental changes throughout stellar evolution. However, accessing these records has proved challenging due to the average grain size (~150 nm) of presolar silicates, their sensitivity to extraction agents and instrumental restrictions, limiting the range of isotopic and chemical signatures which can be studied per grain volume. Here, we present the first known detailed, geochemical study of a presolar silicate from a H-burning supernova, studied in 3D without contributions to the analysis volume and at unprecedented spatial resolutions (< 1 nm), essential for constraining physical and chemical processes occurring within this recently proposed stellar environment. From our results, we infer either; [1] condensation within an environment depleted of heavy elements compatible with the olivine lattice under the pressure and temperature conditions during condensation. [2] during periods of limited mixing either near the end of the pre-supernova phase or from a collapse so rapid localised pockets of different gas compositions formed.</p>
Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning
<div> <p>Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.</p> </div> <div></div>
Magic running and standing wave optical traps for Rydberg atoms - Data and code for analysis
<p>Data, theory calculation and plotting scripts for the publication titled "Magic running and standing wave optical traps for Rydberg atoms" (<a href="https://arxiv.org/abs/2410.20901" target="_blank" rel="noopener">arXiv:2410.20901</a>).</p> <p> </p> <p><strong>File legend</strong></p> <ul> <li> <code>data_FIGx_yyy.mat</code> contains the calculated or measured data used in Figure x</li> <li> <code>calc_FIGx_yyy.py</code> is the script to calculate the theoretical data used in Figure x</li> <li> <code>plot_FIGx_yy.py</code> is the script to create the Figure x of the paper</li> <li> <code>simulation_class.py</code> is a class with theory functions</li> <li> <code>paperstyle.mplstyle</code> is a matplotlib style file</li> <li> <code>requirements.txt</code> lists all the required python packages</li> </ul> <p> </p> <p><strong>Abstract</strong></p> <p>Magic trapping of ground and Rydberg states, which equalizes the AC Stark shifts of these two levels, enables increased ground-to-Rydberg state coherence times. We measure via photon storage and retrieval how the ground-to-Rydberg state coherence depends on trap wavelength for two different traps and find different optimal wavelengths for a 1D optical lattice trap and a running wave optical dipole trap. Comparison to theory reveals that this is caused by the Rydberg electron sampling different potential landscapes. The observed difference increases for higher principal quantum numbers, where the extent of the Rydberg electron wave function becomes larger than the optical lattice period. Our analysis shows that optimal magic trapping conditions depend on the trap geometry, in particular for optical lattices and tweezers.</p> <p> </p> <p><strong>Theory calculation</strong></p> <p>We implemented the potential arising from the Hamiltonians described in the paper. The functions are shared here in the python class <code>simulation_class.py</code>. This class is used in the calculation scripts named <code>calc_FIGx_yyy.py</code> and saves the data as <code>data_FIGx_yyy.mat</code> for the respective Figure x.</p> <p>In case of questions to the code or calculations, please contact Chris Nill or Lukas Ahlheit.</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The experimental data published here are photon storage and retrieval traces of 780 nm probe photons as function of storage duration. We recorded photon traces for different trap laser detunings and Rydberg states.</p> <p>In case of questions to the data, please contact Lukas Ahlheit or Sebastian Hofferberth.</p> <p> </p> <p><strong>Inkscape modification to specific figures</strong></p> <ul> <li>Figure 1: The plotted data is joined in Inkscape with schematic drawings</li> <li>Figure 2: The plot created by the python file is edited in Inkscape for readability</li> <li>Figure 5: We add two schematics into the figure created by the python file</li> </ul>
Data publication for "First-principles derivation and properties of density-functional average-atom models"
<p>Data for the pre-print "First-principles derivation and properties of density-functional average-atom models", https://arxiv.org/abs/2103.09928.</p> <p>Each data folder is named according to the corresponding figure in the paper. For any questions, please contact the authors.</p>
Data for 'Confined vacuum resonances as artificial atoms with tunable lifetime'
<p>This folder contains all the raw data needed to generate the figures in the paper '<em>Confined vacuum resonances as artificial<br> atoms with tunable lifetime</em><em>.</em>' The data are seperated by the figures in which they appear, with a text folder in each folder that contains any relevant additional information. </p>
Original data for publication: The Atomically Precise Gold/Captopril Nanocluster Au25(Capt)18 Gains Anticancer Activity by Inhibiting Mitochondrial Oxidative Phosphorylation
<p> Original data for publication: The Atomically Precise Gold/Captopril Nanocluster Au<sub>25</sub>(Capt)<sub>18</sub> Gains Anticancer Activity by Inhibiting Mitochondrial Oxidative Phosphorylation, ACS Applied Materials & Interfaces</p>
Replication data for: "Ultrafast energy exchange between two single Rydberg atoms on the nanosecond timescale"
<p>Replication data for Figure 3 and 4 of "Ultrafast energy exchange between two single Rydberg atoms on the nanosecond timescale"</p> <p>Preprint at: https://arxiv.org/abs/2111.12314</p> <p> </p>
Data for publication: Nanomechanical and Structural Study of Au38 Nanocluster Langmuir-Blodgett Films Using Bimodal Atomic Force Microscopy and X-Ray Reflectivity
<p>Original data of Figures published in:</p> <p><strong>Nanomechanical and Structural Study of Au<sub>38</sub> Nanocluster Langmuir-Blodgett Films Using Bimodal Atomic Force Microscopy and X-Ray Reflectivity</strong></p> <p>Journal of Colloid and Interface Science, 2022, Michal Swierczewski<sup>,</sup> Alexis Chenneviere, Lay-Theng Lee, Plinio Maroni and Thomas Bürgi*<sup>[</sup></p> <p> </p>
X-ray absorption data and microscopic images of "Atomically dispersed iron(3+) sites catalyze efficient CO2 electroreduction to CO"
<p>XANES and EXAFS data (Figure 1F-H, Figure 3A-B, Figure S2F-H, Figure S3H-I, Figure S10A-B, Figure S11A-B,E-F, Figure S12A, Figure S14D-E)</p> <p>Microscopic images (Figure 1A-D, Figure S2B,D, Figure S4A-B,D-E, Figure S9A-C,E Figure S13A-D)</p> <p>of the research paper 'Atomically dispersed iron(3+) sites catalyze efficient CO2 electroreduction to CO'.</p>
3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning techniques
<p>This repository contains supplementary data to the simulations in our paper</p> <p>"3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning techniques"</p> <p><strong>APTTipCarvingExecutable.tar.gz</strong><br> Contains the production state of the tip synthesis tool source code and compilation</p> <p><strong>TAPSimExecutable.tar.gz</strong><br> Contains the production state of the TAPSim simulation tool source code and compilation</p> <p><strong>scripts.zip</strong><br> Contains tiny shell scripts we used to execute the simulations</p> <p><strong>Two production simulations were performed.</strong><br> Both use the same tip bicrystal geometry but different orientations:<br> <strong>SimID.31054 is the one we discuss in the paper, it has the experimentally measured orientations</strong><br> SimID.31053 is an exemplary simulation with two different crystal orientations</p> <p><strong>For both SimID results five TAR archives exist:</strong><br> TAPSimDetectorHits* contains the main result, the simulated detector hit positionsBiCarving*<br> TAPSimTrajectories* contains all ion trajectories<br> TAPSimInput* contains supplementary results of the TAPSim field evaporation simulation<br> BiCarving* contains the settings and results of the synthesis, the XML file inside the archive details the orientations<br> Meshgen* contains the results of the meshing process prior to the TAPSim simulation</p> <p> </p>
Path-Sensitive Atomic Commit: Local Coordination Avoidance for Distributed Transactions Evaluation Data
<p>Evaluation Data accompanying the paper titled:</p> <p>Path-Sensitive Atomic Commit: Local Coordination Avoidance for Distributed Transactions</p>
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International Brain Laboratory public data
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OpenNeuro
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