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19 results for “reaction conditions”
BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions
<p>Open data for "BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions"</p>
Impact of processing and storage conditions on color stability of strawberry puree: the role of PPO reactions revisited
<p>The effect of pre-heating fresh strawberries (hot break) and the use of refrigerated temperatures prior to pasteurization on the stability of anthocyanins, vitamin C, color and polyphenol oxidase (PPO) activity during storage (42 days at 35 ºC) of strawberry puree was studied. Hot break resulted in 20% residual PPO activity and caused 10% anthocyanin degradation, whereas vitamin C was unaffected. After mashing, purees were stored at 4 ºC and 25 ºC for 3 hours. During this period, anthocyanins and PPO activity remained constant independently of the processing history but ascorbic acid was oxidized faster at 25 ºC. Pasteurization caused complete inactivation of PPO, reduction of anthocyanins (25%), of <em>a*</em> value (6%) and of vitamin C (50%). Neither partial inactivation of PPO early in the processing (hot break) nor the use of refrigeration prior to pasteurization had a positive effect on color and anthocyanin stability of strawberry puree during subsequent storage, suggesting that PPO-derived reaction products formed during processing have a very limited impact on color degradation during shelf-life.</p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</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>
Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes
<p>Yield and Conversion measurements for iodoalkylation reaction of four different terminal alkynes. The reaction conditions as well as the equivalent of the reactants and reagents for each of the three optimizers are listed in the corresponding JSON file. </p>
WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions
<p>This repository houses electronic structure data and metadata generated as part of a computational chemistry case study, enabling full analysis of the paper "WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions" by Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin and Zachary Ulissi.</p>
Supplementary data for "Molecular dynamics study of confined water in the periclase-brucite system under conditions of reaction-induced fracturing"
<p>In this dataset you can find sample data from periclase and brucite simulations and python scripts that can be used to confirm the plots in the paper.</p> <ul> <li>"bruciteSimualtions" and "periclaseSimualtions" contains the simulations where the mineral in contact with water is either brucite or periclase. Within each of these two folders, there are two subfolders, "waterProperties" and "waterThickness". <ul> <li>The data in "waterProperties" is used to calculate water properties. Each folder inside "simulations" represent one simulation.</li> <li>The data in "waterThickness" is used to calculate the change in water film thickness with time under different conditions. The simulations are run for either 3 ns or 10 ns. Each folder inside "simulations_Xns" represent one simulation.</li> </ul> </li> <li>For each folder containing one simulations, we provide: <ul> <li>NAME.run: The input script</li> <li>NAME.data: The input data</li> <li>job.sh: Script to run the simulation</li> <li>log.lammps: Thermodynamic output from the simulation</li> <li>Note that the pressures given in the folder names and in the simulations are in atm, not MPa.</li> </ul> </li> </ul> <p> </p> <ul> <li>"bruciteSimulations" and "periclaseSimualtions" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file. </li> </ul> <p> </p> <ul> <li>The python scripts shows how to extract the relevant data from the lammps log files, which enables reproduction of the figures in the paper. See instructions below to use the scripts.</li> </ul> <p><br> Installation instructions to make the python plot scripts working, assuming you already have numpy and matplotlib:</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git<br> </p> <p>If this does not work, please contact Marthe Grønlie Guren, m.g.guren@geo.uio.no</p>
USPTO Dataset for: Fast Chemical Reaction Condition Suggestion via Rule-Based Classification and Similarity Search
<p>USPTO database that is analyzed with Rxn-INSIGHT (<a href="https://github.com/mrodobbe/Rxn-INSIGHT">https://github.com/mrodobbe/Rxn-INSIGHT</a>).</p><p>This gzip file contains a very large Pandas DataFrame that can be loaded via pd.read_parquet('uspto_rxn_insight.gzip'). Because of the large size of the data, PyArrow version 13.0 must be used. </p><p>To use parquet in Pandas, install PyArrow and fastparquet using pip:</p><p>pip install pyarrow==13.0<br>pip install fastparquet</p>
Chemical reaction between ferropericlase (Mg,Fe)O and water under high pressure-temperature conditions of the deep lower mantle
<p>The XRD datasets and the multigrain dataset for the article "Chemical reaction between ferropericlase (Mg,Fe)O and water under high pressure-temperature conditions of the deep lower mantle" by Yang et al.</p>
Dataset associated to paper: Nanoscaffold effects on the performance of air-cathodes for microbial fuel cells: Sustainable Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions
<p>This file contains the dataset associated to the published research article "Nanoscaffold effects on air-cathode performance in microbial fuel cells: Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions". The dataset contains X-ray powder diffraction, Inductively Coupled Plasma Emission Spectroscopy, elemental analysis, measurements of the specific surface area, transmission electron microscopies, x-ray photoelectron microscopy, electrochemistry and microbial fuel cells power outputs data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements No. 799175 (HiBriCarbon) and No. 748968 (EDGE-FREEMAB). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 13/CDA/2213 and 19/FFP/6761. SI kindly acknowledges support by the Department of Social Justice State Government of Maharashtra, India. </p>
Data file for the paper "General Models for the Electrochemical Hydrogen Oxidation and Hydrogen Evolution Reactions – Theoretical Derivation and Experimental Results Under Near Mass-Transport Free Conditions", J. Phys Chem. C., 2016, DOI:10.1021/acs.jpcc.6b00011
<p>The data in this folder is supplementary information for the paper:<br /> Anthony Kucernak and Christopher Zalitis, "General Models for the Electrochemical Hydrogen Oxidation and Hydrogen Evolution Reactions – Theoretical Derivation and Experimental Results Under Near Mass-Transport Free Conditions", J. Phys Chem. C., 2016, DOI:10.1021/acs.jpcc.6b00011</p> <p>The information is © Anthony Kucernak</p> <p>A description of the models used in these files is provided in that paper. Below is a description of the files</p> <p>Experimental Data.xlsx - This file contains the experimental data used to produce figures 6 and 7 in the aforementioned paper<br /> Model.xlsx - This file contains verified versions of the Heyrovsky-Volmer, Tafel-Volmer and Heyrovsky-Tafel-Volmer mechanisms developed in the paper mentioned above. These excel spreadsheets may be used to fit experimental data user the Solver function in Excel.</p> <p>Heyrovsky-Tafel.cdf, Heyrovsky-Tafel-Volmer.cdf, Tafel-Volmer.cdf, Heyrovsky-Volmer.cdf - These are "computable document format" files produced using Wolfram Mathematica. They allow easy and quick modification of model parameters to allow real-time exploration of the effect of the parameters on current density (as linear and Tafel plots), hydrogen coverage, Effective Tafel slope, and derived parameters. The CDF viewer is availble to download from the Wolfram site - www.wolfram.com</p>
Data from the paper: D. Malko and A. Kucernak, "Kinetic isotope effect in the oxygen reduction reaction (ORR) over Fe-N/C catalysts under acidic and alkaline conditions", Electrochemistry Communications,2017, https://doi.org/10.1016/j.elecom.2017.09.004
<p>Data used to generate the figures in the paper: D. Malko and A. Kucernak, "Kinetic isotope effect in the oxygen reduction reaction (ORR) over Fe-N/C catalysts under acidic and alkaline conditions", Electrochemistry Communications,2017, https://doi.org/10.1016/j.elecom.2017.09.004</p> <p> </p>
MESA files for: "Progenitor stars calculated with small reaction networks should not be used as initial conditions for core collapse"
<p>Reproduction package for RNAAS [TBD].<br><br>Use MESA r24.03.1 and the provided template folder in `MESA_template.tar.xz` to reproduce the MESA models. Resolution tests can be done changing in `inlist1` the parameters `mesh_delta_coeff`, `mesh_time_coeff`, and `mesh_delta_coeff_for_highT` (see commented option).<br>Use the provided `environment.yml` and the script `compare_two_models.py` provided in `scripts.tar.xz` with a few dependencies to reproduce the figure in the research note.</p>
Reaction between serpentinite-equilibrated fluid and sulfide-bearing metagabbo at high P-T conditions
<p>This repository contains the output files of the reaction-path models that simulate the reaction between fluid that is previously in equilibrium with serpentinite and a sulfide-bearing metagabbro over a range of oxygen fugacity. This repository contains supplementary information in support of the manuscript entitled "Mobilization of isotopically heavy sulfur during serpentinite subduction" by Schwarzenbach and coworkers in Science Advances.</p>
Data supplement for: Validating the Nernst--Planck transport model under reaction-driven flow conditions using RetroPy v1.0
<p>This is the repository for the publication's supplementary data and plotting scripts: Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0.</p> <p>The dependency of the scripts can be installed using conda and pip:</p> <pre><code>conda create -n plot numpy matplotlib==3.6.1 h5py python=3.9 conda activate plot pip install palettable</code></pre> <p>To reproduce the figures, execute the files using python:</p> <pre><code>python figure03.py</code></pre> <p> </p>
Fig. 1. Derivatisation reaction conditions for bilocularin A in Synthesis of bilocularin A carbamate derivatives and their evaluation as leucine transport inhibitors in prostate cancer cells
Fig. 1. Derivatisation reaction conditions for bilocularin A (1) and the resulting semi-synthetic carbamate library (2–9).
Data file for the paper "Mechanistic Insights into the Oxygen Reduction Reaction on Metal--N--C Electrocatalysts under Fuel Cell Conditions", ChemElectroChem , 2016, DOI: 10.1002/celc.201600354
<p>Dataset for the above paper.</p>
Data file for the paper "Mechanistic Insights into the Oxygen Reduction Reaction on Metal--N--C Electrocatalysts under Fuel Cell Conditions", ChemElectroChem , 2016, DOI: 10.1002/celc.201600354
<p>Dataset for the above paper</p>
Dataset associated to: Nanoscaffold effects on the performance of air-cathodes for microbial fuel cells: Sustainable Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions
<p>This file contains the dataset associated to the published research article "Nanoscaffold effects on air-cathode performance in microbial fuel cells: Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions". The dataset contains X-ray powder diffraction, Inductively Coupled Plasma Emission Spectroscopy, elemental analysis, measurements of the specific surface area, transmission electron microscopies, x-ray photoelectron microscopy, electrochemistry and microbial fuel cells power outputs data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements No. 799175 (HiBriCarbon) and No. 748968 (EDGE-FREEMAB). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 13/CDA/2213 and 19/FFP/6761. SI kindly acknowledges support by the Department of Social Justice State Government of Maharashtra, India. </p>
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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.