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18 results for “hydrogen storage”
Hydrogen storage properties of Mn and Cu for Fe substitution in TiFe0.9 intermetallic compound - Dataset related to publication.
<p>Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.</p>
Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Dataset related to publication
<p>Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.</p>
Database for machine learning of hydrogen storage materials properties
<p><strong>Database for machine learning of hydrogen storage materials properties</strong></p> <p>Matthew Witman<sup>a</sup>, Mark Allendorf<sup>a</sup>, Vitalie Stavila<sup>a</sup></p> <p><sup>a</sup>Sandia National Laboratories, Livermore, CA</p> <p> </p> <p><strong>Description</strong></p> <p>This ML-HydPARK dataset provides a csv file of metal hydride compositions, capacities, and thermodynamic values that can be used as target properties for building, training, and testing machine learning models. It has been parsed and cleaned from the DOE’s original publicly available HydPARK database according to the procedure in [1] to make it more suitable for immediate use with data-driven models. Generally, this removed duplicate entries, removed entries missing critical data, and attempted to fix various entries with obvious errors in the data. It is continuously updated under version control as new metal alloy hydrides are published in the open literature. Most entries contain data on the enthalpy and entropy of the hydriding reaction, as well the maximum hydrogen capacity, for which compositional machine learning models can be trained [1,2].</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors gratefully acknowledge research support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office through the Hydrogen Storage Materials Advanced Research Consortium (HyMARC). This work was supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy<br> or the United States Government.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Witman, M.; Ling, S.; Grant, D. M.; Walker, G. S.; Agarwal, S.; Stavila, V.; Allendorf, M. D. Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. <em>J. Phys. Chem. Lett</em>. <strong>2020</strong>, 11, 40–47.</li> <li>Witman, M.; Ek, G.; Ling, S.; Chames, J.; Agarwal, S.; Wong, J.; Allendorf, M. D.; Sahlberg, M.; Stavila, V. Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability. <em>Chem. Mater</em>. <strong>2021</strong>, 33, 4067–4076.</li> </ol> <p> </p> <p><strong>Contact</strong></p> <p>Please email <a href="mailto:mwitman@sandia.gov">mwitman@sandia.gov</a> , <a href="mailto:mdallen@sandia.gov">mdallen@sandia.gov</a>, or <a href="mailto:vnstavi@sandia.gov">vnstavi@sandia.gov</a> for questions or to request addition of recent data from the literature to this dataset.</p>
An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]
<p>Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions. </p> <p>Data format: *.opj; *.tif.</p> <p>Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>
TiFe0.85Mn0.05 alloy produced at industrial level for a hydrogen storage plant
<p>Data type: XRD patterns; SEM and EDX results, hydrogen sorption data (pcT-curves, absorption/desoprtion curves). </p> <p>Data format: *.opj; *.tif.; *docx; *jpg</p> <p>Software needed: Origin.</p>
Net load profile for a 10 MWh hydrogen storage system
<p><i>Subject: <strong>Energy</strong></i></p><p><i>Specific subject area: <strong>Hydrogen Storage Modelling</strong></i></p><p><i>Data format: <strong>Raw</strong></i></p><p><i>Data source: <strong>Simulation </strong></i></p><p><i>Data format: <strong>Table</strong></i></p><p><i>Type of data: <strong>Timeseries </strong></i></p><p><i>Date format:<strong> YYYY-MM-GG hh:mm:ss</strong></i></p><p><i>Resolution: <strong>1 hour</strong></i></p><p><i>Units: <strong>Watt [W]</strong></i></p><p> </p><p><strong>Description</strong></p><p>This dataset encompasses 102 net-load, each serving as a guide for either charging or discharging a hydrogen energy storage system with a 0.4 roundtrip efficiency. These profiles correspond to a variety of power energy services.</p><p>Two primary characteristics distinguish each power energy service in the dataset:</p><ul><li>The length of the discharge period refers to the duration over which the stored energy is released.</li><li>The number of charge/discharge cycles within a year indicates how often the service is delivered.</li></ul><p>Although these characteristics vary among the different services, the energy fed to the storage remains constant at 10 MWh per cycle. The unique aspect is the rate at which this stored energy is delivered to fulfill varying power demands, which differs according to each service's specific profile.</p><p>In terms of interpreting the net-load profiles:</p><ul><li>A positive value in a profile signifies a phase of charging, indicating that excess generation is being stored.</li><li>A negative value, on the other hand, denotes a discharge phase, where the storage system delivers a power energy service.</li></ul>
Dataset for "An air-stable Cu(I) metal-organic framework for hydrogen storage"
<p>Dataset for "An air-stable Cu(I) metal-organic framework for hydrogen storage"</p> <p>Raw data set of the electronic structure calculations and inputs for the GCMC calculations relating to above publication.</p>
Hydrogen storage properties of Mn and Cu for Fe substitution in TiFe0.9 intermetallic compound - Raw Dataset related to publication
<p>Data type: Experimental measurements and Rietveld Refinement. Date format: .opj, .pcr, .dat (Software FullProf package outputs). Origin of the data: Experimental x-ray diffraction patterns, and kinetic measurements of hydrogen absorption. Data generated by a Bruker D8 Advance Bragg Brentano diffractometer using Cu-Kα radiation (λ=1.5418 Å), and a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin. Software needed to analyse the data: FullProf package.</p>
Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Raw Dataset related to publication
<p>Data type: Experimental measurements and Rietveld Refinement. Date format: .xls, .xlsm,.opj, .pcr, .dat (Software FullProf package outputs). Origin of the data: Experimental EMPA, x-ray diffraction patterns, and kinetic measurements of hydrogen absorption. Data generated by electron probe micro-analysis (Cameca SX100), a Bruker D8 Advance Bragg Brentano diffractometer using Cu-Kα radiation (λ=1.5418 Å), and a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin and Excel. Software needed to analyse the data: FullProf package.</p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
Supplementary Data for "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage"
<p>This dataset includes optimization and simulation inputs, optimized structures, building blocks, computed hydrogen uptakes and textural properties of the metal–organic frameworks that correspond with work in "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage" (DOI: 10.1021/acs.jpcc.4c00638).</p>
Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application
<p>Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application". The dataset contains relevant data and figures from the publication. Description on how to work with the dataset is given in the readme file. DOI for the original paper: DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3RA07335A">https://doi.org/10.1039/D3RA07335A</a>.</p>
Data supporting Molecular Simulation of Hydrogen Storage and Transport in Cellulose
<p>Data support article accepted for publication in Molecular Simulation. Data includes inputs for crystal relaxation, absorption and diffusion simulations. All processed data required to reproduce plots included and outputs where practicable. Where impractical simulations can be rerun from the inputs provided.</p>
Supplementary material 1 for Thesis Chapter 2 - An obligate aerobe hybridises hydrogen fermentation and carbon storage to adapt to hypoxia
<p>Supplementary material for paired comparative metabolomics and proteomics on <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 during hypoxia, as part of chapter 2 for the thesis "Biochemistry and physiology of mycobacterial adaptations to energy starvation".</p> <p>Description below is identical to that provided in 'Summary.docx'. </p> <p>Proteomics_analysis.xlsx</p> <p>Includes raw and annotated data for comparative proteomics experiments for chapter 2.</p> <p>The tab ‘Annotated comparisons’ contains fold change and p values for the comparisons for each protein from <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 derived from LFQ-Analyst. Functional annotations are derived from KEGG pathways and modules, which utilise the spreadsheets in ‘MSMEG gene annotation’ ‘Protein ids to KEGG pathway’ and ‘KEGG Pathway and Modules’ to link KEGG annotations to MSMEG_XXXX gene identifiers and MSMEG_XXXX to Uniprot ID. Output from LFQ-Analyst is provided in the ‘Full_dataset’, ‘Imputed_matrix’ and ‘Original_matrix’ tabs.</p> <p>Data provided by the Monash Proteomics and Metabolomics Facility for upload into LFQ-analyst are provided as the ‘combined_protein.tsv’ and ‘LFQ-Analyst_experimental_design.txt’.</p> <p> </p> <p>Metabolism_analysis.xlsx</p> <p>Includes annotated data for comparative metabolomics experiments for chapter 2. Within the spreadsheet, TR refers to transition, ST refers to stationary phase and EXP refers to exponential phase. The tabs ‘TRvsEXP’, ‘STvsTR’ and ‘STvsEXP’ contain fold change and p values for each metabolite detected for each comparison. The remaining tabs categorise the metabolites based on KEGG database and IDEOM annotations. For broader categories (‘Lipid metabolism’,’ Carbohydrate metabolism’, ‘Cofactor metabolism’, ‘Nucleotide metabolism’, ‘Amino acid metabolism’ and ‘Peptides’ tabs), annotations were derived directly from filtering the ‘Map’ column of ‘Comparisons’ tab of the IDEOM worksheet (IDEOM_analysis.xlsb). Screenshots are pasted into each tab to show the filtering settings. The remaining tabs comprise narrower categories which were manually annotated with reference to KEGG pathways and maps, and also include rows corresponding to the proteomics data for these categories, so the proteomics and metabolomics data can be interpreted together. The ‘Proteomics’ tab contains the proteomics data referenced by these tabs, which is a copy of the ‘Annotated comparisons’ tab from the ‘Proteomics_analysis.xlsx’ file. A value of ‘N’ indicates the metabolite or protein (at least according to the name in the same row) was not found in these datasets.</p> <p>The IDEOM worksheet (IDEOM_analysis.xlsb) was provided by the Monash Proteomics and Metabolomics Facility and was used for further analysis and for annotations. ‘Data_for_MA_no_normalization.csv’ was also provided by the Monash Proteomics and Metabolomics Facility for upload into Metaboanalyst (https://www.metaboanalyst.ca/).</p>
Pore-Scale Modeling of Carbon Dioxide and Hydrogen Transport during Geologic Gas Storage
<p>This file contains the data discussed in association with our journal submission. We discuss pore-scale simulations to compare CO2 and H2 invasion at subsurface conditions in the context of geologic storage.</p>
Dataset for Underground Hydrogen Storage Simulations
<h1>UHS Dataset</h1> <p>This repositry stores a dataset intended for training machine learning models on the task of predicting underground hydrogen storage. It is the dataset used in the paper ''Deep Learning for Subsurface Flow: A Comparative Study of U-Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage''.</p> <p>The dataset is also available on GitHub: https://github.com/AlvaroC-ML/ml4uhs-dataset.</p>
Supplementary material 2 for Thesis Chapter 2 - An obligate aerobe hybridises hydrogen fermentation and carbon storage to adapt to hypoxia
<p>Supplementary material for cryo-EM tomography experiments.</p> <p>Cryo-EM Aerated Vs Anoxic.pptx contains representations from the best tomogram reconstructions.</p> <p>Anoxic_tomogram.mp4 and Aerated_tomogram.mp4 contain tomogram movies for one sample from each condition.</p> <p> </p>
Dataset "Combining biomass gasification and LOHC mixed gas hydrogenation for high purity hydrogen production and storage"
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