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124 results for “Methanol”
Dataset for the publication: Non-oxidative conversion of methanol to dimethyl ether, methyl formate and dimethoxymethane over Cu/Hβ catalyst: Tailoring product selectivity
<p>The dataset covers the research data of the publication in ChemCatChem with the title "Non-oxidative conversion of methanol to dimethyl ether, methyl formate and dimethoxymethane over Cu/Hβ catalyst: Tailoring product selectivity" (DOI: <a href="https://doi.org/10.1002/cctc.202301704">10.1002/cctc.202301704</a>). The provided data comprehend the main experimental data obtained in the study including material characterisation (XRD, CO-DRIFTS, H2-TPD, pyridine IR, N2 physisorption) and the catalytic data of the investigated Cu-loaded zeolites in a continuous gas-phase fixed-bed reactor.</p>
Atomic coordinates for "Optimizing Surface Active Sites via Burying Single Atom in Subsurface Lattice for Boosted Alkaline Methanol Oxidation"
<p>Atomic coordinates of the optimized computational models in the manuscript of "Optimizing Surface Active Sites via Burying Single Atom in Subsurface Lattice for Boosted Alkaline Methanol Oxidation"</p>
Elucidation of radical- and oxygenate-driven paths in zeolite-catalyzed conversion of methanol and methyl chloride to hydrocarbons
<p>Dataset corresponding to the publication:</p> <p><strong>Elucidation of radical- and oxygenate-driven paths in zeolite-catalyzed conversion of methanol and methyl chloride to hydrocarbons</strong></p> <p>Alessia Cesarini,<sup>1†</sup> Sharon Mitchell,<sup>1†</sup> Guido Zichittella,<sup>1†</sup>* Mikhail Agrachev,<sup>2</sup> Stefan P. Schmid,<sup>2</sup> Gunnar Jeschke,<sup>2</sup> Zeyou Pan,<sup>3</sup> Andras Bodi,<sup>3</sup> Patrick Hemberger,<sup>3</sup>* and Javier Pérez‑Ramírez<sup>1</sup>*</p> <p><sup>1</sup> Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland,</p> <p><sup>2</sup> Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland,</p> <p><sup>3</sup> Laboratory of Synchrotron Radiation and Femtochemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland,</p> <p><sup>†</sup>These authors contributed equally, listed alphabetically.</p> <p>*Corresponding author. E-mails: zguido@mit.edu, <a href="mailto:patrick.hemberger@psi.ch">patrick.hemberger@psi.ch</a>, jpr@chem.ethz.ch.</p>
Flame-made ternary Pd-In2O3-ZrO2 catalyst with enhanced oxygen vacancy generation for CO2 hydrogenation to methanol
<p>Source data for figures displayed in the manuscript.</p>
Water, acetonitrile, and methanol MD simulations driven by many-body ML potentials
<p>Input, output, and trajectories of molecular dynamics (MD) simulations of water, acetonitrile, and methanol. Simulations were driven by many-body machine learning (mbML) potentials including explicit 1-, 2-, and 3-body contributions. <a href="https://keithgroup.github.io/mbGDML/">GDML</a>, <a href="https://libatoms.github.io/GAP/">GAP</a>, and <a href="https://schnetpack.readthedocs.io/en/stable/">SchNet</a> models are provided in a <a href="https://doi.org/10.5281/zenodo.7112163">separate repository</a>. All simulations were performed in the <a href="https://wiki.fysik.dtu.dk/ase/">atomic simulation environment (ASE)</a>. Analyses including radial distribution function (rdf) curves are provided <a href="https://github.com/keithgroup/mbgdml-h2o-meoh-mecn">here</a>.</p> <p><strong>Manifest</strong></p> <p>The following simulations are included in this repository for each solvent.</p> <ul> <li>1 ps hexamer NVE MD simulation driven by MP2/def2-TZVP (in ORCA v4.2.0), mbGDML, mbGAP, mbSchNet, and GFN2-xTB started with the same positions and velocities. Velocities were initialized at 298.15 K with a Maxwell-Boltzmann distribution.</li> <li>Periodic NVT MD simulation at 298.15 K for 10 or 30 ps with a 1 fs time step driven by mbGDML. These simulations contained <ul> <li>58 or <strong>137</strong> water molecules,</li> <li><strong>67</strong> or 122 acetonitrile molecules,</li> <li><strong>61</strong> methanol molecules.</li> </ul> </li> </ul> <p>Systems that are not bolded were used for testing purposes.</p>
Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport
<p><strong>Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Consideration must be given to the rate at which each step in the planned sequence occurs. In many instances, a desired reaction is possible in principle but in practice takes place so slowly as to be ineffective. It is then necessary to investigate whether the rate can be increased to a practicable level by altering the conditions of the reaction, for example, by raising the temperature or by adding an extra species, called a catalyst, that increases the rate without altering the course of the reaction.</p> <p>Streamwise distance (millimeters), Oxidation channel centerline temperature (degrees kelvin)</p> <p>0 376.499</p> <p>0.00025 391.55</p> <p>0.0005 411.347</p> <p>0.00075 432.857</p> <p>0.001 451.349</p> <p>0.00125 465.437</p> <p>0.0015 475.536</p> <p>0.00175 482.591</p> <p>0.002 487.466</p> <p>0.00225 490.805</p> <p>0.0025 493.063</p> <p>0.00275 494.556</p> <p>0.003 495.508</p> <p>0.00325 496.074</p> <p>0.0035 496.366</p> <p>0.00375 496.469</p> <p>0.004 496.424</p> <p>0.00425 496.278</p> <p>0.0045 496.077</p> <p>0.00475 495.832</p> <p>0.005 495.555</p> <p>0.00525 495.26</p> <p>0.0055 494.955</p> <p>0.00575 494.646</p> <p>0.006 494.337</p> <p>0.00625 494.033</p> <p>0.0065 493.735</p> <p>0.00675 493.445</p> <p>0.007 493.164</p> <p>0.00725 492.894</p> <p>0.0075 492.635</p> <p>0.00775 492.386</p> <p>0.008 492.15</p> <p>0.00825 491.925</p> <p>0.0085 491.712</p> <p>0.00875 491.511</p> <p>0.009 491.323</p> <p>0.00925 491.146</p> <p>0.0095 490.98</p> <p>0.00975 490.825</p> <p>0.01 490.682</p> <p>0.01025 490.549</p> <p>0.0105 490.427</p> <p>0.01075 490.314</p> <p>0.011 490.212</p> <p>0.01125 490.119</p> <p>0.0115 490.036</p> <p>0.01175 489.96</p> <p>0.012 489.893</p> <p>0.01225 489.833</p> <p>0.0125 489.781</p> <p>0.01275 489.737</p> <p>0.013 489.699</p> <p>0.01325 489.668</p> <p>0.0135 489.643</p> <p>0.01375 489.624</p> <p>0.014 489.61</p> <p>0.01425 489.603</p> <p>0.0145 489.6</p> <p>0.01475 489.601</p> <p>0.015 489.607</p> <p>0.01525 489.618</p> <p>0.0155 489.632</p> <p>0.01575 489.651</p> <p>0.016 489.673</p> <p>0.01625 489.699</p> <p>0.0165 489.728</p> <p>0.01675 489.76</p> <p>0.017 489.796</p> <p>0.01725 489.834</p> <p>0.0175 489.875</p> <p>0.01775 489.918</p> <p>0.018 489.964</p> <p>0.01825 490.012</p> <p>0.0185 490.063</p> <p>0.01875 490.115</p> <p>0.019 490.169</p> <p>0.01925 490.225</p> <p>0.0195 490.283</p> <p>0.01975 490.343</p> <p>0.02 490.404</p> <p>0.02025 490.467</p> <p>0.0205 490.531</p> <p>0.02075 490.597</p> <p>0.021 490.663</p> <p>0.02125 490.731</p> <p>0.0215 490.8</p> <p>0.02175 490.869</p> <p>0.022 490.94</p> <p>0.02225 491.011</p> <p>0.0225 491.083</p> <p>0.02275 491.155</p> <p>0.023 491.228</p> <p>0.02325 491.301</p> <p>0.0235 491.374</p> <p>0.02375 491.448</p> <p>0.024 491.521</p> <p>0.02425 491.595</p> <p>0.0245 491.668</p> <p>0.02475 491.741</p> <p>0.025 491.813</p> <p>0.02525 491.885</p> <p>0.0255 491.955</p> <p>0.02575 492.025</p> <p>0.026 492.094</p> <p>0.02625 492.161</p> <p>0.0265 492.228</p> <p>0.02675 492.292</p> <p>0.027 492.355</p> <p>0.02725 492.415</p> <p>0.0275 492.474</p> <p>0.02775 492.529</p> <p>0.028 492.583</p> <p>0.02825 492.633</p> <p>0.0285 492.68</p> <p>0.02875 492.724</p> <p>0.029 492.764</p> <p>0.02925 492.801</p> <p>0.0295 492.832</p> <p>0.02975 492.863</p> <p>0.03 492.881</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Data set: methanol formation via oxygen insertion chemistry in ices
<p>This data set corresponds to the experiments appearing in the article "Methanol Formation via Oxygen Insertion Chemistry in Ices" (Bergner, Oberg, & Rajappan, The Astrophysical Journal, 2017, 845:29). Please refer to the article for experimental details & methods.</p> <p>The data set consists of infrared spectra from 600-4000 wavenumbers. Each file contains the spectra taken for a single experiment. The first column holds the spectrum wavenumber values, and all subsequent columns hold the IR absorbance values for spectra taken during ice irradiation. The first row lists column headings, including the time increments (in minutes) of the irradiation spectra. </p> <p>File names are formatted: "IR_N.txt" where N corresponds to the Experiment # listed in Table 1 of the corresponding article. All details about the experiment (irradiation temperature, ice composition) can be retrieved from Table 1.</p>
Neural network potentials for the phosphoester bond formation between phosphate and methanol in water
<p>Neural Network Potentials for H2PO4 and HPO4, as well as the training sets used to train them. </p> <p>The energies are in eV, the forces in eV/angstrom, the xyz coordinates in angstrom, and the box size in angstrom. </p> <p><br>For H2PO4: </p> <p>Number of structures = 386457</p> <p>Number of atoms = 397</p> <p><br><a href="../api/records/11120695/draft/files/coord_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">- coord_H2PO4.npy</a>: npy array containing the xyz coordinates. Array size = (number of structures, number of atoms * 3)</p> <p>- <a href="../api/records/11120695/draft/files/box_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">box_H2PO4.npy</a>: npy array containing the box sizes. Array size = (number of structures, 9)</p> <p>- <a href="../api/records/11120695/draft/files/force_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">force_H2PO4.npy</a>: npy array containing the atomic forces. Array size = (number of structures, number of atoms * 3)</p> <p>- <a href="../api/records/11120695/draft/files/energy_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">energy_H2PO4.npy</a>: npy array containing the full box potential energy. Array size = (number of structures)</p> <p>- <a href="../api/records/11190726/draft/files/type_HPO4.npy/content" target="_blank" rel="noopener noreferrer">type_H2PO4.npy</a>: npy array containing the atom types. Array size = (number of atoms)</p> <p>- <a href="../api/records/11190726/draft/files/type_HPO4.txt/content" target="_blank" rel="noopener noreferrer">type_H2PO4.txt</a>: text file containing the correspondance between the type index, as given in <a href="../api/records/11190726/draft/files/type_HPO4.npy/content" target="_blank" rel="noopener noreferrer">type_H2PO4.npy</a> and the atom name</p> <p>- <a href="../api/records/11120695/draft/files/graph_1_000_compressed_H2PO4.pb/content" target="_blank" rel="noopener noreferrer">graph_1_000_compressed_H2PO4.pb</a>: neural network potential to propagate trajectories.</p> <p>The nomenclature is the same for HPO4.</p> <p>Number of structures = 227881</p> <p>Number of atoms = 396</p>
Infrared-Microwave-Sounding methanol
<p>Monthly daytime methanol (ppbv) for 2008-2018 produced using the Rutherford Appleton Laboratory Infrared-Microwave-Sounding scheme. Further data description can be found in Pope et al. (2021) and the associated supplementary materials. </p> <p>This data has been used in Sands et al. (2024), currently available as a preprint: https://doi.org/10.5194/egusphere-2024-503. </p>
Reaction-Induced Metal-Metal Oxide Interactions in Pd In2O3/ZrO2 Catalysts Drive Selective and Stable CO2 Hydrogenation to Methanol
<p>Ternary Pd-In<sub>2</sub>O<sub>3</sub>/ZrO<sub>2</sub> systems hold promise as industrial catalysts for CO<sub>2</sub>-based methanol synthesis, but maximization of their productivity requires appropriate structuring of the active phase, promoter, and carrier. Here, we report that Pd-In<sub>2</sub>O<sub>3</sub>/ZrO<sub>2</sub> systems prepared by impregnation evolve into a unique catalyst architecture under CO<sub>2</sub> hydrogenation conditions, leading to selective and stable behavior. Detailed space and time-resolved <em>operando</em> characterization and simulations reveal that the restructuring process, completed within the first 30 min under reaction conditions, is governed by the energetics of metal-metal oxide interactions. The resulting architecture comprises InPd<em><sub>x</sub></em> alloy particles decorated by InO<em><sub>x</sub></em> layers, whose proximity is crucial to avoiding performance losses typically observed when palladium agglomerates. The findings highlight the potential beneficial role of reaction-induced restructuring in advancing catalyst design.</p>
Dataset - Improved Isolation of Microbiologically Produced (2R,3S)-Isocitric Acid by Adsorption on Activated Carbon and Recovery with Methanol
<p>This dataset in form of an Excel-format contains the data of the journal article “Improved Isolation of Microbiologically Produced Isocitric Acid by Adsorption on Activated Carbon and Recovery with Methanol”. The dataset shows the values of different experiments, calculations carried out for this publication.</p>
Variational Vibrational States of Methanol (12D): Dataset
<p>This dataset collects the coefficients larger than 1.0E-3 for the direct products of the basis functions, and their excitation numbers for the vibrational wavefunction computed with b=8 and ave-L, regarding the results discussed in the paper titled "Variational Vibrational States of Methanol (12D)" by Ayaki Sunaga, Gustavo Avila, and Edit Mátyus.</p>
GC-MS data corresponding to: Methanol-Based Esterification of Palm Oil Sludge – Preparation of Fatty Acids (Palmitic and Oleic) Ethyl Esters via Ethyl Acetate Transesterification by Javier Chaparro-Acosta and Juan-Manuel Urbina-González
<p>GC-MS data corresponding to:</p> <p>Methanol-Based Esterification of Palm Oil Sludge – Preparation of Fatty Acids (Palmitic and Oleic) Ethyl Esters via Ethyl Acetate Transesterification<br> by<br> Javier Chaparro-Acosta<sup>(1)</sup> and Juan-Manuel Urbina-González<sup>(2*)</sup></p> <p><sup>1</sup>Escuela de Ingeniería Química, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia<br> <sup>2</sup>Escuela de Química, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia<br> * Correspondence: jurbina@uis.edu.co<br> <br> <strong>Abstract</strong><br> Acid catalyzed Fischer esterification of fatty acids using methanol (as reagent and solvent) allow the preparation of long chain alkyl methyl esters. Transesterification of palm oil in basic media using methanol is also a known path to monoalkyl methyl ethers derived of fatty acids. In this work we report the Fischer esterification using methanol of a sample of local palm oil sludge (a fraction rich in fatty acids) and how during the extraction with ethyl acetate a transesterification reaction occurred, allowing the preparation of ethyl esters of oleic and palmitic acids as main compounds.</p> <p> </p>
Many-body machine learning models for water, acetonitrile, and methanol
<p><a href="https://keithgroup.github.io/mbGDML/">GDML</a>, <a href="https://libatoms.github.io/GAP/">GAP</a>, and <a href="https://schnetpack.readthedocs.io/en/stable/">SchNet</a> models trained on 1-, 2-, and 3-body energies and forces of water, acetonitrile, and methanol. Size-transferable <a href="https://github.com/mir-group/nequip">NequIPs</a> are trained on trimer data. Energies and forces were computed at the MP2/def2-TZVP level of theory in ORCA v4.2.0. Data sets, training scripts, and analyses of these potentials are available <a href="https://github.com/keithgroup/mbgdml-h2o-meoh-mecn">here</a>. Applications of these models on molecular dynamics simulations are found <a href="https://doi.org/10.5281/zenodo.7112198">here</a>.</p> <p><strong>Changelog</strong></p> <p>The format is based on <a href="https://keepachangelog.com/en/1.0.0/">Keep a Changelog</a>, and this project adheres to <a href="https://semver.org/spec/v2.0.0.html">Semantic Versioning.</a></p> <p>[0.0.2] - 2022-12-20</p> <p>Added</p> <ul> <li><a href="https://github.com/mir-group/nequip">NequIPs</a> trained for all solvents using 1000 trimers.</li> </ul> <p>[0.0.1] - 2022-09-25</p> <ul> <li>Initial release!</li> </ul> <p> </p>
Effect of temperature in methanol conversion to dimethyl ether (DME) and light hydrocarbons with beta zeolite
<p><span>Crystalline beta zeolite molecular sieve with SiO<sub>2</sub>/Al2O<sub>3</sub> molar ratio of 28.5 was synthesized by the hydrothermal crystallization method and examined for the methanol dehydration reaction. The micro-mesoporous beta zeolite was active between 250 and 450°C. Dimethyl ether (DME) was observed as the predominant product at all reaction temperatures, with a maximum selectivity of 47.9% at 300°C and a methanol turnover frequency (TOF<sub>MeOH</sub>) of 741.9 h-1. At increased reaction temperatures, the beta zeolite showed enhanced strong acid site fraction, promoting higher hydrocarbon formation following the olefin-based cycle. It was revealed that the crystallinity, porosity, and acidity of the beta zeolite change in the reaction environment. Amorphous carbon deposition occurred on beta zeolite, which involved the loss in crystallinity to some extent. The temperature increase showed a pore-broadening phenomenon at elevated temperature regions. The regeneration cycle test showed stable activity of regenerated beta zeolite for 280 h time-on-stream.</span></p>
Results dataset for research paper "Ultra-long-duration energy storage anywhere: methanol with carbon cycling"
<p>Results dataset for research paper <a href="https://doi.org/10.1016/j.joule.2023.10.001">Ultra-long-duration energy storage anywhere: methanol with carbon cycling</a>. The code is also available on <a href="https://github.com/PyPSA/methanol-uldes">GitHub</a>. Results include both the raw PyPSA NetCDF networks as well as summary files for each run. The main runs from the paper are in the directory "final-main". Runs for the other countries (Ireland, France, Sweden) are in "final-IEFRSE". Runs without wind power are in "final-nowind".</p>
The impact of methanol on behaviour: Dataset from zebrafish (Danio rerio) behavioural research
Open the record for dataset details and reuse information.
Effect of temperature in methanol conversion to dimethyl ether (DME) and light hydrocarbons with beta zeolite
Open the record for dataset details and reuse information.
Kinetics of Photoelectrochemical Oxidation of Methanol on Hematite Photoanodes
<p>The kinetics of photoelectrochemical (PEC) oxidation of methanol, as a model organic substrate, on α-Fe<sub>2</sub>O<sub>3</sub> photoanodes are studied using photoinduced absorption spectroscopy and transient photocurrent measurements. Methanol is oxidized on α-Fe<sub>2</sub>O<sub>3</sub> to formaldehyde with near unity Faradaic efficiency. A rate law analysis under quasi-steady-state conditions of PEC methanol oxidation indicates that rate of reaction is second order in the density of surface holes on hematite and independent of the applied potential. Analogous data on anatase TiO<sub>2</sub> photoanodes indicate similar second-order kinetics for methanol oxidation with a second-order rate constant 2 orders of magnitude higher than that on α-Fe<sub>2</sub>O<sub>3</sub>. Kinetic isotope effect studies determine that the rate constant for methanol oxidation on α-Fe<sub>2</sub>O<sub>3</sub> is retarded ∼20-fold by H/D substitution. Employing these data, we propose a mechanism for methanol oxidation under 1 sun irradiation on these metal oxide surfaces and discuss the implications for the efficient PEC methanol oxidation to formaldehyde and concomitant hydrogen evolution.</p>
Final geometries and energies, statistical analysis and estimated errors of single metals and bimetallics for CO2 to methanol conversion
<p>The dataset accommodate all the extra data discussed in:<br>Pisal, P., Krejčí, O. & Rinke, P. Machine learning accelerated descriptor design for catalyst discovery in CO<sub>2</sub> to methanol conversion. <em>npj Comput Mater</em> <strong>11</strong>, 213 (2025). https://doi.org/10.1038/s41524-025-01664-9 </p> <p>The datased contains four types of data:</p> <ol> <li>All the final geometries and energies of adsorbated (*H, *O, *OCHO & *OCH3) and all the 158 single metals and bimetallic alloys on all the surfaces with Miller indices in {-2, -1, ... 2} optimized with Open Catalyst Project (OCP) 20 <em>equiformer_V2</em> machine-learned force-field model. These are in the <a href="https://zenodo.org/api/records/15587232/draft/files/geometries_and_energies.zip/content" target="_blank" rel="noopener noreferrer">geometries_and_energies.zip</a> file organized by the metal/alloys name, with the final geometries and enerigies in a json file, using a json ASE format.</li> <li>All the estimated mean absolute errors (MAE) of predicted adsorption energies for all the considered metals and bimetallic alloys in <a href="https://zenodo.org/api/records/15587232/draft/files/Estimated_MAEs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">Estimated_MAEs_metals_bimetallics.csv</a> and xlsx file. The data content is identical, files differs only by a format.</li> <li>All the adsorption energy disctibutions (AEDs) for all the 158 metals/alloys and adsorbates in <span><a href="https://zenodo.org/api/records/15587232/draft/files/AEDs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">AEDs_metals_bimetallics.csv</a></span> and xlsx files. The data content is identical, files differs only by a format.</li> <li>All the statistical information of the adsorption energies for all the 158 metals/alloys and adsorbates in <span><a href="https://zenodo.org/api/records/15587232/draft/files/Statistics_AEDs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">Statistics_AEDs_metals_bimetallics.csv</a></span> and xlsx files. The data content is identical, files differs only by a format.</li> </ol>
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