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212 results for “Adsorption”
Dataset for Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics"</strong></p><p>published in ChemElectroChem</p><p>Specifically, the following measurements are provided:</p><ul><li>Scanning electrochemical cell microscopy (SECCM). Cyclic voltammetry (E, i) data for each location across the sample. 5 cycles.</li><li>Chronoamperometry (i, t) for the anodization process.</li><li>Atomic Force Microscopy (AFM) topography.</li><li>Raman microscopy</li><li>X-ray photoelectron spectroscopy (XPS)</li><li>Scanning electron microscopy (SEM)</li></ul>
Accelerated lignocellulosic molecule adsorption structure determination dataset
<p>Dataset containing all structures from the accelerated structure search for lignocellulosic molecules. Part of the data corresponds to DFT data, while the largest portion of structures correspond to data acquired using a machine learned interatomic potential (NequIP) trained on the former. The energies attached to each structure are atomisation energies. Contains both isolated adsorbates and adsorption structures. The dataset also contains configuration files for the NequIP training. </p>
Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles
<p>This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments. </p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
NETTAG+ Data set on adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants in fishing nets
<p>This data set includes the raw data associated with the article in <em>Marine Pollution Bulletin</em><strong> "</strong>Potential of fishing nets for adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants"</p>
Datasets for Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes
<p>All used Datasets to pair with "Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes" by Nicholas Ouassil*, Rebecca L. Pinals*, Jackson Travis Del Bonis-O'Donnell, Jeffrey W. Wang, and Markita P. Landry</p> <p>*Co-authors</p>
AdsMT: Multi-modal Transformer for Predicting Global Minimum Adsorption Energy
<p>We built three Global Minimum Adsorption Energy (GMAE) benchmark datasets named OCD-GMAE, Alloy-GMAE and FG-GMAE from OC20-Dense, Catalysis Hub, and `functional groups' (FG)-dataset datasets through strict data cleaning, and each data point represents a unique combination of catalyst surface and adsorbate. These new benchmark datasets can be beneficial for future ML study on GMAE prediction.</p> <p>In addition, a similar data cleaning procedure was employed on the OC20 dataset to create a new dataset named OC20-LMAE, which comprises surface/adsorbate pairings along with their local minimum adsorption energies (LMAE). The OC20-LMAE dataset contains 363,937 data points and serves as an effective resource for model pretraining.</p>
Databases with structures used for "Improving the Activity of M-N4 Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption"
<p>DFT optimised structures used for the paper "Improving the Activity of M-N<sub>4</sub> Catalysts for the Oxygen Reduction Reaction by Electrolyte Adsorption". There is a separate database for structures with Cr, Mn, Fe and Co as the central metal atom in the M-N4 motif, and one with the molecular references. The structures can be retrieved using the Atomic Simulation Environment (ASE).</p>
Protein adsorption on biodegradable mikro/nanofibre materials for tissue engineering
<p><span>Due to their specific properties, nanofibrous materials are increasingly used in regenerative medicine and tissue engineering. Nanofibrous materials can be used as tissue scaffolds for injured (damaged) tissue. The main factor for tissue scaffolds is their biocompatibility. One of the main factors influencing the organism's physiological response is the interaction of the material with proteins. Proteins adsorbed on the material's surface give the tissue scaffolds a "biological identity"</span><span><span>. Cells in the organism subsequently interact with proteins adsorbed on the material's surface and determine the entire organism's response to the implanted material. This work deals with the influence of the morphology and chemical composition of polyester nanofibrous materials on the adsorption of proteins. The materials produced by electrospinning (DC spinning) were characterised from the point of view of morphology and wettability. Then, the adsorption of weakly and strongly bound proteins on the fibre surface was evaluated. Cell adhesion and proliferation on the tested materials were also observed. The results of protein adsorption were compared with the results of cell adhesion and proliferation to determine the effect of the amount of adsorbed proteins on the interaction of cells with the tested materials.</span></span></p>
Dataset: Post-treatment of high-rate activated sludge effluent via zeolite adsorption and recovery of ammonium-nitrogen
<p>Dataset used to write journal article (doi:10.1016/j.biortech.2024.130837) covering the post-treatment of high-rate activated sludge effluent via zeolite adsorption and recovery of ammonium-nitrogen to produce potential alternative fertilising products. The data included is data gathered from column experiments (for breakthrough modelling and to compare different N recovery methods). Metal and cation content results for the treated wastewater and adsorption outputs is also included</p>
Dataset: Dendritic nanoarchitecture imparts ZSM-5 zeolite with enhanced adsorption and catalytic performance in energy applications
<p>The development of zeolites possessing dendritic features represents a great opportunity for the design of novel materials with applications in a large variety of fields and, in particular, in the energy sector to afford its transition towards a low carbon system. In the current work, ZSM-5 zeolite showing a dendritic 3D nanoarchitecture has been synthesized by the functionalization of protozeolitic nanounits with an amphiphilic organosilane, which provokes the branched aggregative growth of zeolite embryos.<br> Dendritic ZSM-5 exhibits outstanding accessibility arising from a highly interconnected network of radially-oriented mesopores (3 – 10 nm) and large cavities (20 – 80 nm), which add to the zeolitic micropores, thus showing a well-defined trimodal pore size distribution. These singular features provide dendritic ZSM-5 with sharply enhanced performance in comparison with nano- and hierarchical reference materials when tested in a number of energy related applications, such as VOCs (toluene) adsorption (improved capacity), plastics (low-density polyethylene) catalytic cracking (boosted activity) and hydrogen production by methane catalytic decomposition (higher activity and deactivation resistance).</p>
Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry
<p>This data publication is based on the metadata and datasets underlying the manuscript "Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry" (<a href="https://doi.org/10.1021/acsanm.3c04330"><span>https://doi.org/10.1021/acsanm.3c04330</span></a>)</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K, N2 77K), Water Intrusion measurements, NMR Relaxometry and solid state MAS NMR measurements. More information can be found in the Readme file.</p> <p> </p>
Dataset for: On the Nature of Hydrophobic Organic Compound Adsorption to Smectite Minerals Using the Example of Hexachlorobenzene-Montmorillonite Interactions
<p><br>This dataset contains all data obtained from first principle DFT calculations at the PBE-D3 DFT level<br>by the program VASP for the paper published in the journal "Minerals". Please cite this article when using the dataset.<br><br>Title: "On the Nature of Hydrophobic Organic Compound Adsorption<br>to Smectite Minerals Using the Example of Hexachlorobenzene-Montmorillonite Interactions."</p> <p>Authors: Peter Grancic, Leonard Böhm, Martin H. Gerzabek, and Daniel Tunega <br>DOI10.3390/min13020280. </p> <p><br>The model systems are Ca-montmorillonite (Ca-Mt) models with varying layer charge from the Mg/Al substitutions.<br>Calculated are interaction energies of hexachlorobenzene (HCB) with Ca-Mt models in various configurations.<br>QE.dat file is file with collected energies of all models, Collect_QE.py is a selfmade python file for the collection of energies.<br>The structure of all directories is as following:<br>HCBCaxxx directories contain optimized HCB-CaMt complexes<br>HCBCaxxx_Clay directories contain pure CaMt models<br>HCBCaxxx_HOC directories contain only HOC molecule<br>Each directory contains:<br>-input geometry data (POSCAR file)<br>-optimized geometries (CONTCAR.norm file)<br>-input parameters for VASP (INCAR file)<br>-k-points (KPOINTS file)<br>-complete output files (OUTCAR.norm and vasprun.xml.norm files)<br>POTCAR file with pseudopotentials are not provided due to copyright rights.<br>Their type can be found in OUTCAR or vasprun.xml files.</p> <p>Funding: This work has been supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), grant number 443637168, BO5388/1–1 and Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung, FWF), grant number I 4876–N in the bilateral project ”Clay minerals as sorbents for hydrophobic organic chemicals – ClayHOC”. The results<br>presented have been achieved using the Vienna Scientific Cluster (VSC), project number 70544.</p> <p>Terms of use: These data are provided "as is", without any warranty. This dataset is provided under the Creative Commons Attribution 4.0 International license.</p>
Dataset for article: Adsorption of the hydrophobic organic pollutant hexachlorobenzene to phyllosilicate minerals
<p>This repository contains data obtained from first principle DFT calculations at the PBE-D3 DFT level<br>by the program VASP for the research article </p> <p><br>Title: "Adsorption of the hydrophobic organic pollutant hexachlorobenzene to phyllosilicate minerals"<br>published in Environmental Science and Pollution Research (2023) 30:36824–36837.</p> <p>Authors: Leonard Böhm, Peter Grančič, Eva Scholtzová, Benjamin Justus Heyde, Rolf-Alexander Düring, Jan Siemens, Martin H. Gerzabek & Daniel Tunega.</p> <p>Please cite that article when using this dataset.</p> <p>The systems in the dataset are models of Me-montmorillonite layers (Me = Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba)<br>interacting with hexachlorobenzene (HCB) molecule. Calculated are interaction energies of optimized geometries of HCB...Me-Mnt complexes. Dateset contains tables with collected calculated adsorption energies and main geometrical paramters.<br>The structure of dataset is following:<br>Rep_Ads_I directory contains directories for HCB molecule, and for complexes of HCB with Li-Mnt to Rb-Mnt. Each directory of complexes contains corresponding directory of isolated Me-Mnt layer. The second directory, Rep_ads_II has the same structure as Rep_Ads_I directory for Me=Mg, Ca, Sr, and Ba.<br>In each directory are the main files for VASP calculations:<br>input geometry data (POSCAR.norm file)<br>optimized geometry (CONTCAR.norm file)<br>input parameters for VASP (INCAR file)<br>k-points (KPOINT file)<br>complete output files (OUTCAR.norm and vasprun.xml.norm files)<br>POTCAR file with pseudopotentials are not provided due to copyright restrictions. Their type can be found in OUTCAR file or vasprun.xml file.</p> <p>Funding: This work has been supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), grant number 443637168, BO5388/1–1 and Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung, FWF), grant number I 4876–N in the bilateral project ”Clay minerals as sorbents for hydrophobic organic chemicals – ClayHOC”. The results<br>presented have been achieved using the Vienna Scientific Cluster (VSC), project number 70544.</p> <p>Terms of use: These data are provided "as is", without any warranty. This dataset is provided under the Creative Commons Attribution 4.0 International license.</p>
Data for 'Graphene oxide aerogels for gas phase adsorption and selective separation of aromatic hydrocarbons and cycloalkanes'
<p>### Selected experimental data for the 'Graphene oxide aerogels for gas phase adsorption and selective separation of aromatic hydrocarbons and cycloalkanes ###</p> <p>Authors of the manuscript related to the uploaded data:<br>1. Maksymilian Plata-Gryl, Department of Process Engineering and Chemical Technology, Faculty of Chemistry, Gdansk University of Technologym, email: maksymilian.plata-gryl@pg.edu.pl<br>2. Roberto Castro-Muñoz, Department of Sanitary Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, email: food.biotechnology88@gmail.com<br>3. Emilia Gontarek-Castro, Department of Environmental Technology, Faculty of Chemistry, University of Gdansk<br>4. Alan Miralrio, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey<br>5. Grzegorz Boczkaj, Department of Sanitary Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, email: grzegorz.boczkaj@pg.edu.pl</p> <p>Package contains following data:<br>1. Fourier-transform infrared spectra of rGOA, GO, and graphite samples, format: .csv, number of files: 5<br>2. Raman spectra of rGOA samples, format: .csv, number of files: 3<br>3. Low temperature nitrogen adsorption-desorption isotherms, format: .txt, number of files: 4<br>4. Raw chromatograms of test probes for rGOA samples, format .txt, number of files: 234 in 12 subfolders</p> <p>For more information about experimental conditions or data please see the manuscript/publication or contact author/s.</p>
Trajectories of RNA adsorption on a curved surface S1 S2
<p>Coarse-Grained simulations of two RNA fragments:</p> <p>- 22 nts. (Hairpin)</p> <p>- 40 nts. (Ext. Hairpin w/ Bulge)</p>
QMC Raw Data for Graphene Corrugation Effects during Helium Adsorption
<h2>Graphene Corrugation Data Files</h2> <p> </p> <p>Raw quantum Monte Carlo data and submission scripts for a publication studying the effects of graphene corrugation on helium adsorption. </p> <p>View README.md for more information on repository contents. Updated version (2024-04-28) contains larger system sizes (N_G = 72,108).</p> <p>Fully corrugated lookup tables for isotropic graphene can be found here: https://zenodo.org/records/6574043</p>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening
<p>This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset.</p> <p>The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain) calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format.</p> <p>The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF).</p> <p>A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts:</p> <p>1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx).</p> <p>2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).</p> <p> </p> <p>The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.</p> <p> </p> <p>If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.</p>
Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by combining Adsorption, Liquid Intrusion and solid-state NMR spectroscopy
<p>This data publication is based on the metadata and datasets underlying the manuscript "</p> <p><span>Assessment of Hydrophilicity/Hydrophobicity in Mesoporous Silica by Combining Adsorption, Liquid Intrusion, and Solid-State NMR Spectroscopy (</span>"https://doi.org/10.1021/acs.langmuir.3c03516")</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K), Water Intrusion measurements, solid state MAS NMR measurements. and molecular dynamics simulations. </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.