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19 results for “CO2 hydrogenation”
From Lab to Technical CO2 Hydrogenation Catalysts: Understanding PdZn Decomposition
<p>Supplementary material: Additional experimental results, including catalytic performances of the different scaled-up samples, SEM images, and additional XAS data related to the linear combination fit</p>
A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
<p>Thermocatalytic CO<sub>2</sub> hydrogenation to methanol is an attractive decarbonization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In<sub>2</sub>O<sub>3</sub>-, and ZnO-ZrO<sub>2</sub>-based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (<em>R</em><sup>2</sup> > 0.85) are developed to predict the methanol space-time yield (<em>STY</em>) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g<sub>MeOH</sub> h<sup>−1</sup> g<sub>cat</sub><sup>−1 </sup>between the actual and predicted methanol<em> STY</em>. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization.</p>
Supplementary Material for Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2
<p>Supplementary Material for "Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO<sub>2</sub>"</p>
Data for Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)
<p>Data, scripts, and all generated mechanisms for the preprint and article "Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO<sub>2</sub> Hydrogenation on Ni(111)"</p>
The promotional role of Mn in CO2 hydrogenation over Rh-based catalysts from a surface organometallic chemistry approach
<p>Rh-based catalysts modified by transition metals have been intensively studied for CO2 hydrogenation due<br> to their high activity. However, understanding the role of promoters at the molecular level remains<br> challenging due to the ill-defined structure of heterogeneous catalysts. Here, we constructed welldefined RhMn@SiO2 and Rh@SiO2 model catalysts via surface organometallic chemistry combined with thermolytic molecular precursor (SOMC/TMP) approach to rationalize the promotional effect of Mn in CO2 hydrogenation. We show that the addition of Mn shifts the products from almost pure CH4 to a mixture of methane and oxygenates (CO, CH3OH, and CH3CH2OH) upon going from Rh@SiO2 to RhMn@SiO2. In situ X-ray absorption spectroscopy (XAS) confirms that the MnII is atomically dispersed in the vicinity of metallic Rh nanoparticles and enables to induce the oxidation of Rh to form the Mn–O–Rh interface under reaction conditions. The formed interface is proposed to be key to maintaining Rh+ sites, which is related to suppressing the methanation reaction and stabilizing the formate species as evidenced by in situ DRIFTS to promote the formation of CO and alcohols<br> </p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
CO2 hydrogenation to methanol and hydrocarbons over bifunctional Zn-doped ZrO2/zeolite catalysts
<p>Supplementary material: N2 adsorption, PXRD, IR, modelling, test results, XAS, SEM, PES, TEM</p>
Highly selective hydrogenation of CO2 to propane over GaZrOx/H-SSZ-13 composite
<p>Suppelementary data: Synthesis, testing data, N2 absorption, PXRD, XPS, EDS, TEM, SEM, IR, DFT, TGA, GC-MS, 27Al NMR, source data for graphs (Excel files)</p>
Raw data set for Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production.
<p>This repository contains the raw data and code used to generate the results in the paper:</p> <p>Tanzer S.E., Blok K., Ramirez Ramirez A. Negative Emissions in the Chemical Sector: Lifecycle CO2 Accounting for Biomass and CCS Integration into Ethanol, Ammonia, Urea, and Hydrogen Production. 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15. 2021. doi: 10.2139/ssrn.3819778.</p> <p>also published as chapter 4 in the PhD dissertation ”Negative Emissions in the Industrial Sector”. The PhD was the department of Engineering Systems and Services, Faculty of Technology Policy, Management at the Delft University of Technology, between 2017-2022. </p> <p>This is intended to be a record of the exact data and code used to generate the results and graphics used in this publication. It is not necessarily designed for user-friendliness or tested to work on other machines and may contain extraneous data and files.</p> <p>To make use of the python black box modelling library for your own work, please check out the most recent public release, which can be found at https://zenodo.org/record/5800104#.YjUTnC8w30o</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>
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>
Data for Kinetic Effect of In Situ Water Adsorption during CO2 Hydrogenation: An Experimental Investigation
Open the record for dataset details and reuse information.
A solid Xantphos macroligand based on porous organic polymers for the catalytic hydrogenation of CO2
<p>This dataset contains all raw data to the manuscript "A solid Xantphos macroligand based on porous organic polymers for the catalytic hydrogenation of CO2" of Nisters et al.</p> <p>The file format is based on the Software OriginLab. A free tool is available to access and visualize the data. see: https://www.originlab.com/viewer/</p>
Data set for the journal article "Surface Intermediates in In-Based ZrO2-Supported Catalysts for Hydrogenation of CO2 to Methanol"
<p>Raw data for the article " Surface Intermediates in In-Based ZrO<sub>2</sub>-Supported Catalysts for Hydrogenation of CO<sub>2</sub> to Methanol", already published in the Journal of Physical Chemistry C, DOI: <a href="https://doi.org/10.1021/acs.jpcc.1c08814">https://doi.org/10.1021/acs.jpcc.1c08814</a></p> <p>Folder names describe the type of data content. All details concerning conditions and equipment for measurements can be found in the main text and supporting information of the article.</p>
Hyper-crosslinked polyphosphines as microporous macroligands for catalytic CO2 hydrogenation – a systematic study on structure-activity relations
<p>This dataset contains all raw data to the manuscript "Hyper-crosslinked polyphosphines as microporous macroligands for catalytic CO2 hydrogenation – a systematic study on structure-activity relations" of <span>Arne Nisters, Steffen Schleuning, Gerd Buntkowsky, Torsten Gutmann, Marcus Rose.</span></p>
Activating 2D MoS2 by loading 2D Cu-S nanoplatelets for improved visible light photocatalytic hydrogen evolution, drug degradation, and CO2 reduction
<p>Please see ref:</p> <p> </p> <p>Temerov, F.; Greco, R.; Celis, J.; Eslava, S.; Wang, W.; Yamamoto, T.; Cao, W. <em>Results Mater.</em> <strong>2024</strong>, 22, 100569</p>
Atom-by-atom design of Cu/ZrOx clusters on MgO for CO2 hydrogenation using liquid-phase atomic layer deposition
<p>Source data for the following publication: </p> <p><strong>Atom-by-atom design of Cu/ZrOx cluster on MgO for CO2 hydrogenation using liquid-phase atomic layer deposition,<em> Nature Catalysis</em>, 2024.</strong></p>
Tuning the macroligand environment of a solid ruthenium phosphine catalyst for the hydrogenation of CO2 to formate
<p>This dataset contains all raw data to the manuscript "Tuning the macroligand environment of a solid ruthenium phosphine catalyst for the hydrogenation of CO2 to formate" of Arne Nisters, Nils Heim, Marcus Rose.</p>
Data set for the journal article "Hydrogen dissociation sites on indium-based ZrO2-supported catalysts for hydrogenation of CO2 to methanol"
<p>Raw data for the article " Hydrogen dissociation sites on indium-based ZrO2-supported catalysts for hydrogenation of CO2 to methanol", already published in Catalysis Today, DOI: https://doi.org/10.1016/j.cattod.2021.04.010</p> <p>Folder names describe the type of data content. All details concerning conditions and equipment for measurements can be found in the main text and supporting information of the article.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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