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15 results for “CO2 capture”
Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies
<p>Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication <em>Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO<sub>2</sub> Capture Technologies </em>by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information. </p> <p> </p>
Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation"
<p>Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation".</p> <p><strong>Versions of dataset:</strong></p> <p><strong><span>V1: </span></strong><span>First dataset regarding the data used in the article.</span></p> <p><strong><span>V2:</span></strong><span> The dataset </span><span>was newly reorganized</span><span>, containing the </span><span>data,</span><span> that </span><span>were used</span><span> for the published article. </span><span>More information can be found</span><span> in the README file.</span></p> <p><strong>Article abstract</strong></p> <p>The urgent need for sustainable and innovative approaches to mitigate the increasing levels of atmospheric CO<sub>2</sub> necessitates the development of efficient methods for its removal. In this study, we focus on the new, innovative approach for synthesis and functionalization of metal-organic framework (MOF) ZIF-8 in one step at room temperature to enhance its capacity for CO<sub>2</sub> capture. Specifically, we investigated the impact of four amino-compounds, namely tetraethylenepentamine (TEPA), hexadecylamine (HDA), <a title="Learn more about ethanolamine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemical-engineering/ethanolamine">ethanolamine</a> (ELA), and cyclopropylamine (CPA), on the <a title="Learn more about chemical structure from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/structure-composition">chemical structure</a>, size, surface area and porosity, and CO<sub>2</sub> capturing of ZIF-8 powder. By varying concentrations of the amino-compounds, we examined their influence on the ZIF-8 properties. Our findings demonstrate that each amino-compound and its respective concentration exhibit distinct effects on the characteristics of ZIF-8. Notably, the ZIF-8 sample functionalized with the highest presented concentration of TEPA exhibited significant improvement in CO<sub>2</sub> trapping efficiency, with a 33.3% enhancement. Moreover, least concentrated samples with added HDA or CPA demonstrated notable improvements with enhancements of 46.6% and 18.6%, respectively. These results highlight the potential of simple synthesis and functionalization techniques for MOFs in enhancing their CO<sub>2</sub> capture capabilities. The findings from this study offer new opportunities for the development of strategies to mitigate CO<sub>2</sub> emissions using MOFs.</p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
A new and highly robust light-responsive Azo-UiO-66 for highly selective and low energy post-combustion CO2 capture and its application in a mixed matrix membrane for CO2/N2 separation
<p>Supporting information for publication in Journal of Materials Chemistry A, <a href="https://dx.doi.org/10.1039/C8TA03553A">https://dx.doi.org/10.1039/C8TA03553A </a></p>
An Insight on the Effect of Azobenzene Functionalities Studied in UiO-66 Framework for Low Energy CO2 Capture and CO2/N2 Membrane Separation
<p>Data supporting journal manuscript. Includes an Excel spreadsheet with all the raw data for the figures in the manuscript, calibrations and calculations. Also includes 3D chemical structures of MOF units, and SEM images of MOF/polymer composite materials.</p> <p><strong>Abstract</strong></p> <p>In this paper, we report a simple approach to study the fundamental aspect of light-responsive metal organic framework (MOF) in UiO-66 topology through a mixed-ligand approach. Apart from change in the structural property, the loading of azobenzene linker inside the framework also affects the CO<sub>2 </sub>light-responsive property and CO<sub>2</sub>/N<sub>2 </sub>selectivity which could help to design future low-energy CO<sub>2 </sub>adsorbents. Further study to incorporate the MOFs into mixed matrix membranes also indicates the benefit of higher azobenzene loading in the MOF to enhance the CO<sub>2</sub>/N<sub>2 </sub>separation performance since it can improve the separation performance which could not be obtained in non-functionalized fillers. </p> <p><strong>Preprint manuscript</strong></p> <p>A Preprint version of the manuscript is available at: <a href="https://doi.org/10.26434/chemrxiv.7568696.v1">https://doi.org/10.26434/chemrxiv.7568696.v1</a> </p> <p><strong>Published article</strong></p> <p>Journal of Materials Chemistry A, 2019, DOI: <a href="https://doi.org/10.1039/C9TA02096A">https://doi.org/10.1039/C9TA02096A</a></p>
Supplementary information for D4.6 CEMCAP comparative techno-economic analysis of CO2 capture in cement plants
<p>Supplementary information for D4.6 CEMCAP comparative techno-economic analysis of CO2 capture in cement plants</p>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Parameters for light-driven pH swing CO2 capture
<p>The repository contains the parameters for modeling the light-driven pH swing CO2 capture process. The .json-files are readable with <a href="https://feos-org.github.io/feos/">FeOs</a> as follows:</p> <blockquote> <pre><code>from feos.epcsaft import ElectrolytePcSaftParameters, IdentifierOption from feos.eos import EquationOfState from feos.dippr import Dippr molecules = [] # list of molecule names, to be filled saft_param = ElectrolytePcSaftParameters.from_json(substances=molecules, pure_path=FILENAME_PURE_PARAMS, binary_path=FILENAME_BINARY_PARAMS, identifier_option=IdentifierOption.Name) # create equation of state eos = EquationOfState.epcsaft(parameters=saft_param).dippr(Dippr.from_json(substances=molecules, pure_path=FILENAME_IG_PARAMS, identifier_option=IdentifierOption.Name))</code></pre> </blockquote> <ul> <li>pure_parameters.json: Contains ePC-SAFT parameters and permittivity parameters.</li> <li>binary_parameters.json: Contains binary interaction parameters kij.</li> <li>ideal_gas_parameters.json: Contains ideal gas parameters for DIPPR Equation 107.</li> </ul>
Dataset: Carbonate Regeneration Using a Membrane Electrochemical Cell for Efficient CO2 Capture
<p>Dataset for journal publication "Carbonate Regeneration Using a Membrane Electrochemical Cell for Efficient CO2 Capture," published in ACS Sustainable Chemistry & Engineering (DOI: 10.1021/acssuschemeng.2c04175). Data are organized according to figure number.</p>
Solid-state NMR data for the publication "Bonding of Polyethylenimine in Covalent Organic Frameworks for CO2 Capture from Air"
<p>This upload contains the solid-state NMR of COF-709 series obtained by using Bruker Avance IV NEO 400 MHz NMR instrument. The instrument used in this work was supported by the National Science Foundation under Grant No. 2018784. Per NSF requirements, the data will be publicly available at the time of publication. This dataset can be opened and processed by using MestreNova NMR or similar softwares.</p>
ASE Database of CO2 Electro Capture on Redox-Active Metal-Organic Frameworks
<p>ASE database containing simulated structures for 1D, 2D and 3D conductive metal-organic frameworks.</p>
Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture
<p>Dataset associated with the publication "Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture".</p> <p> </p> <p>Changelog:</p> <p>- Include sample input files for GCMC using RASPA2 and geometry optimization using LAMMPS.</p>
MOOC: ''The development of new technologies for CO2 capture and conversion''
<p>This MOOC on ''The development of new technologies for CO2 capture and conversion'' is given by international professors. Full playlist: https://youtube.com/playlist?list=PLc88uGJ1RIFWKci0m0opuUSQSUfPylYKU</p> <p>MODULE 1: CCUS Carbon Capture Storage and Utilization approach within CO2MPRISE project, introduction by Gabriele Mulas (UNISS).</p> <p>MODULE 2: Mechanochemical activation of Olivine, introduction by Sebastiano Garroni, senior researcher at University of Sassari (UNISS).</p> <p>MODULE 3: Innovative catalytic materials, introduction by Vasiliki Alexiou, project manager at Monolithos Ltd.</p> <p>MODULE 4: Metal hydrides, by Fabiana Gennari, researcher at Comisión Nacional de Energía Atómca (CNEA) – Argentina. || Chapter 4.1: Material synthesis and processes implementation.</p> <p>MODULE 5: Solid-oxides and graphene based systems for CO2 activation and conversion, by Francisco Gracia, professor at University of Chile.</p> <p>MODULE 6: Diffraction experiments for material characterization, by Stefano Enzo, senior researcher at University of Sassari (Italy).</p>
Design Strategies for Oxy-Combustion Power Plant Captured CO2 Purification
<p>This submission contains Aspen Plus files for the design and systems performance analysis of oxy-combustion power plant captured CO2 purification using different techniques.</p> <p>This data is taken from https://psecommunity.org/LAPSE:2021.0570</p>
Uncovering the CO2 Capture Mechanism of NaNO3-Promoted MgO by 18O Isotope Labeling
<p>Raw data for the article "Uncovering the CO<sub>2</sub> Capture Mechanism of NaNO<sub>3</sub>-Promoted MgO by <sup>18</sup>O Isotope Labeling", already published in JACS Au</p> <p>All details concerning conditions and equipment for measurements can be found in the main text, supporting information of the article, and in text file contained in the folder.</p>
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International Brain Laboratory public data
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OpenNeuro
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