Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
266
datasets available to search
ShareScore release 0.9.0
Dataset results
266 results for “Catalyst”
Data set for the journal article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion"
<p>In the article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO<sub>2</sub>-to-CO conversion" we demonstrated that Fe impurities in a hybrid CoPc@MWCNT catalyst lead to its performance deterioration during long-term CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. The data are divided into four groups:<br> (i) Current transients and gas chromatography data for short-term electrolysis at different potentials (in an excel file we give the numbers of chromatograms for each potential; current transients are given as an origin file with datasets and plots inside)<br> (ii) Current transients and gas chromatography data for long-term electrolysis at different potentials and with different catalysts (in respective excel files we give the numbers of chromatograms; figure numbers are given in the folder names)<br> (iii) Electron microscopy images and EDX datasets (the images and datasets are collected in the folders with respective figure numbers used in the paper)<br> (iv) Calibration curves for ICP-MS</p>
Controlled Formation of Dimers and Spatially Isolated Atoms in Bimetallic Au-Ru Catalysts via Carbon-Host Functionalization
<p>Enclosed we report the data in the article: "Controlled Formation of Dimers and Spatially Isolated Atoms in Bimetallic Au-Ru Catalysts via Carbon-Host Functionalization" by Pérez-Ramírez et al.</p>
nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources
<p>This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images.</p> <p>The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images. </p>
Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction
<p>The dataset contains raw data that complements the article:</p> <p>High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081.</p> <p>C. Iffelsberger and M. Pumera*</p> <p>https://doi.org/10.1039/D1TA05581J</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNOx Catalysts by Machine Learning-Assisted X‑ray Absorption Spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements and related elaboration from Figures 1-4 of the corresponding article</li> <li>Files are with filename extensions: <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <p>In situ XANES and EXAFS data were collected at the BM23 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) in a Microtomo reactor cell; measured Cu-CHA samples are indicated in the following with “Cu/Al”-“Si/Al” labels</p> <ul> <li><strong>fig_01_XANES:</strong> Normalized Cu K-edge XANES for Cu-CHA samples 0.1-5; 0.5-15; 0.6-29, collected at 200 °C after pretreatment in O<sub>2</sub>, reduction in NO+NH<sub>3</sub> and subsequent oxidation in O<sub>2</sub>.</li> <li><strong>fig_02_Conversion:</strong> NOx conversion in the 150−500 °C temperature range for Cu-CHA samples 0.1-5, 0.5-15, 0.6-29; TOF at 200 °C versus fraction of Cu(I) from XANES LCF after oxidation and fraction of Cu(I) from XANES LCF after oxidation versus Cu density for the same catalysts.</li> <li><strong>fig_03_EXAFS_FT_WT:</strong> Magnitude of experimental EXAFS spectra, obtained by Fourier transforming k<sup>2</sup>χ(k) spectra in the 2.4−12.0 Å<sup>−1</sup> range for Cu-CHA samples 0.1-5, 0.5-15, 0.6-29 after reduction in NO+NH<sub>3</sub> and subsequent oxidation in O<sub>2</sub>; corresponding EXAFS WT maps magnified in high-R range (2-4 Å), obtained using a Morlet WT with parameters (σ=1, η=7).</li> <li><strong>fig_04_EXAFS_MLfit:</strong> Magnitude of experimental and best fit EXAFS spectra, obtained by Fourier transforming k<sup>2</sup>χ(k) spectra in the 2.4−12.0 Å<sup>−1</sup> range for Cu-CHA samples 0.1-5, 0.5-15, 0.6-29 after oxidation in O<sub>2</sub>. Scaled components 1 ([Cu<sup>I</sup>(NH<sub>3</sub>)<sup>2</sup>]<sup>+</sup>), 2 and 3 (planar and bent μ-η<sup>2</sup>,η<sup>2</sup>-peroxo diamino dicopper(II)) isolated by ML-assisted EXAFS fitting are also reported, vertically translated.</li> <li><strong>Information on</strong>:</li> <li>specialized abbreviations: <strong>CHA</strong>– chabazite; <strong>XANES</strong>– X-ray absorption near edge structure, <strong>EXAFS</strong> – Extended X-ray absorption fine structure; <strong>LCF</strong> – Linear Combination Fit;<strong> FT</strong>: Fourier Transform; <strong>WT</strong> – Wavelet Transform; <strong>ML</strong> – Machine Learning; <strong>TOF</strong> – Turn Over Frequency;</li> </ul>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Structure Sensitivity in the Electrocatalytic Reduction of CO2 with Gold Catalysts
<p>Dataset for the manuscript:</p> <p>Mezzavilla, Stefano, Sebastian Horch, Ifan E. L. Stephens, Brian Seger, and Ib Chorkendorff. “Structure Sensitivity in the Electrocatalytic Reduction of CO <sub>2</sub> with Gold Catalysts.” <em>Angewandte Chemie International Edition</em>, February 11, 2019. <a href="https://doi.org/10.1002/anie.201811422">https://doi.org/10.1002/anie.201811422</a>.</p> <p> </p> <p>The following files have been uploaded:</p> <p>1) "raw-data- figures and tables" - Excel file with all the data used in the figures and tables (both main text and SI)</p> <p>2) "Exerimental Methods" - Word file with the details of all the experimental methods used in the work</p> <p> </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>
Continuous process technology for glucoside production from sucrose using a whole cell-derived solid catalyst of sucrose phosphorylase
<p>We provide here the underlying data of the publication "Continuous process technology for glucoside production from sucrose using a whole cell-derived solid catalyst of sucrose phosphorylase". Please find the abstract below.</p> <p>Advanced biotransformation processes typically involve the upstream processing part performed continuously and interlinked tightly with the product isolation. Key in their development is a catalyst that is highly active, operationally robust, conveniently produced and recyclable. A promising strategy to obtain such catalyst is to encapsulate enzymes as permeabilized whole cells in porous polymer materials. Here, we show immobilization of the sucrose phosphorylase from Bifidobacterium adolescentis (P134Q-variant) by encapsulating the corresponding E. coli cells into polyacrylamide. Applying the solid catalyst, we demonstrate continuous production of the commercial extremolyte 2-α-D-glucosyl-glycerol (2-GG) from sucrose and glycerol. The solid catalyst exhibited similar activity (≥70%) as the cell free extract (~800 U g-1 cell wet weight) and showed excellent in-operando stability (40 °C) over 6 weeks in a packed-bed reactor. Systematic study of immobilization parameters related to catalyst activity led to the identification of cell loading and catalyst particle size as important factors of process optimization. Using glycerol in excess (1.8 M), we analyzed sucrose conversion dependent on space velocity (0.075 – 0.750 h-1) and revealed conditions for full conversion of up to 900 mM sucrose. The maximum 2-GG space-time yield reached was 45 g L-1 h-1 for a product concentration of 120 g L-1. Collectively, our study establishes a step-economic route towards a practical whole cell-derived solid catalyst of sucrose phosphorylase, enabling continuous production of glucosides from sucrose. This strengthens the current biomanufacturing of 2-GG, but also has significant replication potential for other sucrose-derived glucosides, promoting their industrial scale production using sucrose phosphorylase. </p>
Synthesis of Phenol-Tagged Ruthenium Alkylidene Olefin Metathesis Catalysts for Robust Immobilisation Inside Met-al-Organic Framework Support
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Catalysts</em> <strong>2023</strong>, <em>13</em>(2), 297; <a href="https://doi.org/10.3390/catal13020297">https://doi.org/10.3390/catal13020297</a></p> <p>The research was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860322 for the ITN-EJD “Coordination Chemistry Inspires Molecular Catalysis” (CCIMC) and by the National Science Centre, Poland (OPUS grant 2017/27/B/ST5/00941).</p>
Kinetics assessment of the homogeneously catalyzed hydroformylation of ethylene on a Rh-catalyst
<p>Supplementary Information. Section S1, description of dependence of rate and equilibrium coefficients on selected standard state, reaction entropies of reaction steps at different standard states, and derivation of rate equations; Section S2, experimental conditions of data sets selected for model regression and model validation; Section S3, calculation of enthalpies and entropies of solvation explained in more detail and obtained values; and Section S4, calculation of entropies of coordination explained in detail and resulting values</p>
Dataset for Quantitative description of metal center organization in single-atom catalysts
<p>Dataset for <strong>Quantitative description of metal center organization in single-atom catalysts </strong>by by K. Rossi, A. Ruiz-Ferrando, D. Faust Akl, V. Gimenez Abalos, J. Heras-Domingo, R. Graux, X. Hai, J. Lu, D. Garcia-Gasulla, N. López, J. Pérez-Ramírez, and S. Mitchell.</p> <p>The data is structured as follows:</p> <ul> <li>01_Micrographs: all micrographs employed in .tif and .png format. An <a href="https://imagej.net/">imageJ </a>macro to overlay coordinate files with images is attached.</li> <li>02_Ground_truth: contains the manually-labeled and predicted xy-coordinates of atomic positions including probabilities.</li> <li>03_All_detection_data: contains all automated predictions of atomic positions in the images of this study (uhd), and of Mitchell et. al in <em>JACS</em>, <strong>144</strong>, 8018-8029 (2022) (jacs_train, jacs_test). This folder further contains model weights and area segmentations needed to estimate the surface atomic densities.</li> <li>04_Trimetallic_analysis: Figures complementing Supplementary Figure S21.</li> </ul> <p> </p>
DFT optimised structure used for the paper "Cation Insertion to Break the Activity/Stability Relationship for Highly Active Oxygen Evolution Reaction Catalyst"
<p>DFT optimised structures used to calculate the OER activities in "Cation Insertion to Break the Activity/Stability Relationship for Highly Active Oxygen Evolution Reaction Catalyst". The structures are bundled in two databases, LiIrO3.db which contains all structures for alpha-LiIrO<sub>3</sub> and KLiIrO3-disordered.db which contains all the structures for the disordered Li<sub>0.75</sub>K<sub>0.25</sub>(H<sub>2</sub>O)<sub>0.50</sub>IrO<sub>3 </sub>structure. The structures can be retrieved using the Atomic Simulation Environment (ASE, https://wiki.fysik.dtu.dk/ase/index.html). The keywords 'ads' and 'surface' can be used to search the structure, e.g. surface='Z-step' and ads='*OOH' will give the structure with OOH adsorbed on the Z-step surface (see paper for details on the different surfaces).</p>
N2-sorption and catalytic results of doped Cu-catalysts for glycerol hydrogenolysis
<p>This repository includes experimental data associated with the following publication: A. Bouriakova et al., "Dopant induced stabilization of alumina supported copper – impact on the catalytic performance in the hydrogenolysis of glycerol to 1,2-propanediol", submitted to Catalysis Communications in April 2020</p> <p> </p> <p>In this work, five catalysts supported on γ-Al<sub>2</sub>O<sub>3</sub> were studied, i.e. one bare Cu and four doped-Cu catalysts. The catalysts were prepared via sequential impregnation. The nominal dopant loading was 1wt%, for the Cu a nominal value of 10wt% was selected:</p> <p>- 10wt% Cu/-Al2O3, indicated as Cu</p> <p>- 1wt%Ba-10wt% Cu/-Al2O3, indicated as Ba-Cu<br> - 1wt%Ce-10wt% Cu/-Al2O3, indicated as Ce-Cu<br> - 1wt%Cs-10wt% Cu/-Al2O3, indicated as Cs-Cu<br> - 1wt%La-10wt% Cu/-Al2O3, indicated as La-Cu</p> <p> </p> <p>The catalysts were investigated for 68 h for continuous flow pure glycerol hydrogenolysis in a trickle bed regime at 473 K, 7.5 MPa, 135 kg<sub>cat </sub>s mol<sup>-1</sup><sub>glycerol </sub>and 7 mol<sub>H2</sub> mol<sup>-1</sup><sub>glycerol</sub>, and compared for glycerol conversion, 1,2-propanediol selectivity and stability.</p> <p>The data file contains the glycerol conversion and selectivities over time for the studied catalysts, in combination with N<sub>2</sub>-sorption data of those catalysts. In addition, an overview of literature data on glycerol hydrogenolysis is given (Fig 1 in the manuscript).</p>
Catalyst sites and active species in the early stages of MTO conversion over cobalt AlPO-18 followed by IR spectroscopy
<p>Supplementary material: Ex-situ DR-UV-visible spectroscopy, Ex-situ FT-IR Spectroscopy. In-situ FT-IR Spectroscopy, In continuo FT-IR Spectroscopy, Brønsted acidity of SAPO-18 </p>
PdZn/ZrO2+SAPO-34 bifunctional catalyst for CO2 conversion: Further insights by spectroscopic characterization
<p>Supplementary material: atomic concentrations calculated from XPS, XPS spectra</p>
Synthesis of [Mim][OTf]-TiO2 catalyst - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents the synthetic procedure of [Mim][OTf]-TiO2 catalyst.</p>
Dataset to Why Hydrogen Dissociation Catalysts do not Work for Hydrogenation of Magnesium by Selim Kazaz et al.,, Fig. 1, 2, 3, 4 , 5 ,6
<p>Datasets to publication S. Kazaz et al, Adv. Sci. 2023, 2304603c; Why Hydrogen Dissociation Catalysts do not Work for Hydrogenation of Magnesium; additional data/explanation upon request (corresponding authors)</p>
Dataset for "From CO2 to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-Alloyed GaInSn Catalyst"
<p>Experimental raw data for the article "<em>From CO<sub>2</sub> to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-alloyed GaInSn Catalyst</em>", published in <em>Journal of Physical Chemistry C </em>(2024). DOI:10.1021/acs.jpcc.4c05482.</p> <p>The data set is organized according to the publication's figures. XPS data given here is the raw data without binding energy calibration. For the manuscript, binding energies in a series of samples were calibrated by taking the strongest peak, where no chemical shift was to be expected, as a reference.</p>
Whole cell-based catalyst for enzymatic production of the osmolyte 2-O-α-glucosylglycerol
<p>We provide here the underlying data of the publication "Whole cell-based catalyst for enzymatic production of the osmolyte 2-O-α-glucosylglycerol". Please find the abstract below.</p> <p><strong>Background: </strong>Glucosylglycerol (2‑O‑α‑D‑glucosyl‑sn‑glycerol; GG) is a natural osmolyte from bacteria and plants. It has promising applications as cosmetic and food‑and‑feed ingredient. Due to its natural scarcity, GG must be prepared through dedicated synthesis, and an industrial bioprocess for GG production has been implemented. This process uses sucrose phosphorylase (SucP)‑catalyzed glycosylation of glycerol from sucrose, applying the isolated enzyme in immobilized form. A whole cell‑based enzyme formulation might constitute an advanced catalyst for GG production. Here, recombinant production in <em>Escherichia coli</em> BL21(DE3) was compared systematically for the SucPs from <em>Leuconostoc mesenteroides</em> (LmSucP) and <em>Bifidobacterium adolescentis</em> (BaSucP) with the purpose of whole cell catalyst development.<br> <strong>Results:</strong> Expression from pQE30 and pET21 plasmids in <em>E. coli</em> BL21(DE3) gave recombinant protein at 40–50% share of total intracellular protein, with the monomeric LmSucP mostly soluble (≥80%) and the homodimeric BaSucP more prominently insoluble (~40%). The cell lysate specific activity of LmSucP was 2.8‑fold (pET21; 70±24 U/mg; N=5) and 1.4‑fold (pQE30; 54±9 U/mg, N=5) higher than that of BaSucP. Synthesis reactions revealed LmSucP was more regioselective for glycerol glycosylation (~88%; position O2 compared to O1) than BaSucP (~66%), thus identifying LmSucP as the enzyme of choice for GG production. Fed‑batch bioreactor cultivations at controlled low specific growth rate (µ=0.05 h<sup>−1</sup>; 28°C) for LmSucP production (pET21) yielded ~40 g cell dry mass (CDM)/L with an activity of 2.0×10<sup>4</sup> U/g CDM, corresponding to 39 U/mg protein. The same production from the pQE30 plasmid gave a lower yield of 6.5×10<sup>3</sup> U/g CDM, equivalent to 13 U/mg. A single freeze–thaw cycle exposed ~70% of the intracellular enzyme activity for GG production (~65 g/L, ~90% yield from sucrose), without releasing it from the cells during the reaction.<br> <strong>Conclusions:</strong> Compared to BaSucP, LmSucP is preferred for regioselective GG production. Expression from pET21 and pQE30 plasmids enables high‑yield bioreactor production of the enzyme as a whole cell catalyst. The freeze– thaw treated cells represent a highly active, solid formulation of the LmSucP for practical synthesis.</p>
ScienceDex guides
Understand access before you commit
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.