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102 results for “Dissolution”
Dataset for Towards improved online dissolution evaluation of Pt-alloy PEMFC electrocatalysts via electrochemical flow cell - ICP-MS setup upgrades
<p>Experimental data comprises raw data from ICP-MS (Inductively coupled plasma mass spectrometry) (i.e. time dependence of signal intensity for Co59 and Pt195) for different cell geometry and operating parameters. <br>Model data comprise of time- and space-dependent values of Pt ions concentration in the modelling cell and local velocity vectors.</p>
Silica solubility and dissolution kinetics at high saline geothermal conditions
<p>This dataset contains solubility data for silica as a function of time, temperature and salinity. The dataset supports Chapter 2 in the deliverable “Report on mineral solubility and precipitation at high salinities, DOI: https://doi.org/10.48440/gfz.4.8.2023.001 from the H2020 project REFLECT.</p> <p>The silica material used as solid substrate for the dissolution studies was pro analysis sea sand (purified by acid washing and calcinated for analysis) from Merck. The sand grain size (125-250 µm) included in the experiments was obtained by sieving the material. The sieved powder was washed with tap water to remove fine grains from the samples, and dried prior to experiments. The BET surface area of the sand was measured to 0.69 m<sup>2</sup>/g and the weighted mean particle size distribution (PSD) was 118 µm. The crystallographic structure was determined by X-ray diffraction analysis (XRD) and this analysis showed that the sample contained mainly low-quartz (minimum 95% w/w) with a few unidentified impurities. SEM/EDS maps of the silica powder showed essentially pure silica with minor Al impurity. Some grains or regions are enriched in Al and K, suggesting some aluminium silicate. Some minor spots rich in Ti, Fe and Cr were also detected.</p> <p>The experiments conducted to study silica solubility at equilibrium conditions were performed at five different temperatures (100, 125, 150, 175 and 200°C) and four salinities (NaCl concentrations 50.9, 103.6, 215.7 and 338.1 g/kg H<sub>2</sub>O). The columns containing SiO<sub>2</sub> and NaCl solutions where isolated for a reaction time of six days before fluid sampling (Table1 “Silica solubility at high saline geothermal conditions”).</p> <p>The experiments conducted to study silica solubility kinetics were performed for different time periods (from 1 hour up to 144 hours) to study solubility as a function of time. These tests were conducted at 200°C with NaCl concentration 50.92 g/kg and 338.09 g/kg H<sub>2</sub>O (Table2 “Silica solubility kinetics at high saline geothermal conditions”).</p> <p>The experimental setup consists of packed static columns. Maximum four columns (length 40 cm, i.d. 10.22 mm, stainless steel SS316) packed with the material to study can be placed in parallel within the setup. Porous metal frits (HC276) are placed at the outlet and inlet of the columns to prevent entrainment of the material. Approximately 50 g of dried SiO<sub>2</sub> powder is required to fill a column completely and the pore volume was measured gravimetrically to be approximately 15 ml. Two Gilson 307 high performance liquid chromatography (HPLC) pumps are included in the setup. One for filling and displacing column pore fluid and one for diluting the fluid prior to sampling, preventing precipitation of dissolved silica due to depressurization and cooling. Pressure was maintained by a dome loaded backpressure regulator (BPR) from CoreLab at the column outlet and liquid samples were collected using a fraction collector (Gilson FC203B). The setup of columns and inlet/outlet valves was placed in a heating cabinet (Memmert). The columns were thermally insulated to prevent instabilities in temperature and hence pressure when opening the heating cabinet during sampling.</p> <p>The columns are flooded with degassed NaCl fluid at a low flow rate and pressurized initially to 25 bars while temperature is increased slowly to the desired level. The time of start is noted, the brine pump is shut off, and the individual columns isolated by closing inlet and outlet valves. After a period (hours, days, or weeks) samples are withdrawn from the columns and diluted at the mixing point by re-opening the valves and operating both HPLC pumps. A dilution factor of 8.5 is selected to prevent precipitation. For each sampling five samples of 2 ml is collected (totally 10 ml of fluid). The two first samples are considered to contain mainly dead volumes from tubing, fittings and valves and are therefore discharged. The three last samples represent the pore fluid from the column. These samples are analysed for Si and NaCl concentration. The NaCl concentration was analysed to keep control of the dilution step of the sampling process.</p> <p>SiO<sub>2</sub> and NaCl concentrations were analysed using inductively coupled plasma mass spectrometry (ICP-MS) or inductively coupled plasma optical emission spectrometry (ICP-OES). The elements Si and Cl (ICP-MS) or Si and Na (ICP-EOS) were detected.</p> <p>The Si concentration from the analysis was reported as mg/L solution. From this concentration the concentration of SiO<sub>2</sub> in the samples were calculated and reported as mol/kg H<sub>2</sub>O. The conversion from liter solution to kg H<sub>2</sub>O was done using the OLI software for density calculations.</p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Silica dissolution and precipitation kinetics in hot geothermal conditions
<p>This dataset report quartz dissolution kinetics as obtained from packed column experiments at different flow rates. Variables were temperature, pressure and NaCl content as incicated in the table. Silica values are reported as mg/L of Si as measured by ICP-OES. Also included in the table is a column describing how data series were treated to extract steady-state values for each flow rate (cf. the report to which the current dataset is related). The column "solubility used" states the solubility used to calculate dissolution (k<sub>+</sub>) and precipitation (k<sub>-</sub>) rate constants along with a column "source" which briefly indicates how this value was obtained. Further details are given in the report.</p> <p>Factors used to get from the raw data to the reported rate constants are also given. Not included in the table, but common for all data points are a quartz BET surface are of 0.6922 m<sup>2</sup>/g, 10 g quartz and a quartz activity assumed to be 1.</p> <p>Note that this dataset contain several measurement points that are not representative. These include points close do equilibrium where kinetic information cannot be reliably obtained and points where it is suspected that a temperature drop during sampling may have caused erroneous results (the Si content actually represents a somewhat lower temperature that was not measured). The reader is referred to the full report for details.</p>
Growing Diamonds in the Laboratory to investigate Growth, Dissolution, and Inclusions Formation processes
<p>Dataset for the manuscript : <strong>Growing Diamonds in the Laboratory to investigate Growth, Dissolution, and Inclusions Formation processes</strong></p><p><strong>after </strong>Hélène Bureau, Imène Estève, Caroline Raepsaet, Geeth Manthilake</p><p>It comprises one excel file containing raw SEM EDX data and 10 SEM images of the samples</p>
Channeling: a new class of dissolution in complex porous media
<p>ModelAandBGeometries.7z contains the original 12,000 x 12,000 pixel geometries created for Models A and B in Menke et al. 2022 PNAS. They were subsequently binned by 12 in each direction and padded by 2 on all sides to get the 1,004 x 1,004 pixel geometries input into GeoChemFoam. The original location and radius of each bead is supplied in the .hdf5 file as 'rad', 'x_coor', and y_coor'. </p> <p>ModelA_Pe##_K##.hdf5 and ModelB_Pe##_K##.hdf5 contain all of the simulation results for each flow and reaction scenario. This includes porosity, permeability, time_s, concentration, velocity, pores, grains, throats, and moments for all output timesteps. Pore2 & throat2 denote analyses with the fully dissolved section of the model excluded. </p> <p>The model (GeoChemFoam) used to run these dissolution scenarios can be downloaded with tutorials at https://github.com/GeoChemFoam/. The script used to make the micromodel geometries can be found at https://github.com/hannahmenke/PNAS2022.</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Dataset for "A Bayesian neural network predicts the dissolution of compact planetary systems"
<p>The dataset used for training and evaluating the models in the paper "A Bayesian neural network predicts the dissolution of compact planetary systems": https://arxiv.org/abs/2101.04117. </p> <p>The code for working with this dataset, and other links, can be found at: https://github.com/MilesCranmer/bnn_chaos_model.</p>
Visualization of Dissolution-Precipitation Processes in Lithium-Sulfur Batteries: Supporting Data
<ul> <li>Contours_1.gif: 0 mA/g - pristine state</li> <li>Contours_2.gif: 30 mA/g</li> <li>Contours_3.gif: 80 mA/g</li> <li>Contours_4.gif: 130 mA/g</li> <li>Contours_5.gif: 180 mA/g</li> <li>Contours_6.gif: 230 mA/g</li> <li>Contours_7.gif: 330 mA/g - no remaining solid sulphur</li> </ul>
Silica dissolution under a flow of pure water at pressure and temperature conditions relevant to geothermal energy extraction
<p>This dataset is a published product of the 'REFLECT' Project - a Horizon Europe project which aims to inform the processes of geothermal energy extraction by determining the effect of relevant fluid properties and reactions in order to enhance predictive geochemical modelling and thus the energy exploitation and life-time of geothermal power plants.</p><p>The dataset records the concentration of silica measured in water that had been passed through a packed column of quartz grains at temperatures from 200 to 450°C and pressures from 150 to 450 bar. Concentrations are reported as g/ml SiO2, measured photometrically. The reader is referred to the full report for deliverable 1.4 of the REFLECT project for details of the experimental set up and interpretation of the data.</p><p>Silica concentrations marked with (a) are believed to be artificially reduced compared to the rest of the dataset due to a reduction in the surface density of active sites during some of the highest dissolution experiments. The final column lists the chronological order in which the measurements were taken to assist with interpretation of this factor.</p><p>The density marked with (b) represents the density of water at 150 bar and 342°C, rather than the measured condition of 148 bar and 344°C. This is to reflect the fact that the solubility measurement suggests the presence of a liquid phase - the conditions chosen represent the closest point on the phase boundary to the measured conditions. The discrepancy may reflect a small shift in the phase envelope due to silica dissolution in addition to any uncertainty in the <i>pT</i> measurements.</p>
Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study
<p>Data sets for the Publication 'Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study' in Water Resources Research.</p>
Dataset: Hybrid Polarizing Solids with Extended Pore Diameters for Dissolution Dynamic Nuclear Polarization
<p>This dataset contains raw NMR, EPR, and relaxometry data, N2 adsorption-desorption isotherms for best HYPSOs, and all of the codes, used for data processing and figures in the article.</p>
Text-fig. 9. a. Shorn-off, vertically embedded conch surrounded by layer of crinoid debris at level of planation of shell and with some debris within the conch at this level. Lateral width of body-chamber 80 mm. b. Example of ammonite that occurs rarely in the Main Cenoceras Bed. Note the poorly defined shell particularly on the outer whorl, suggesting partial dissolution. Tape measure for scale. in 'Cenoceras Islands' In The Blue Lias Formation (Lower Jurassic) Of West Somerset, Uk: Nautilid Dominance And Influence On Benthic Faunas
Text-fig. 9. a. Shorn-off, vertically embedded conch surrounded by layer of crinoid debris at level of planation of shell and with some debris within the conch at this level. Lateral width of body-chamber 80 mm. b. Example of ammonite that occurs rarely in the Main Cenoceras Bed. Note the poorly defined shell particularly on the outer whorl, suggesting partial dissolution. Tape measure for scale.
Supplemental material for "Computational study of the dissolution of cellulose into single chains: the role of the solvent and agitation"
<p>Input-scripts and data-files from the Cellulose article "Computational study of the dissolution of cellulose into single chains: the role of the solvent and agitation" by Bering, E., Torstensen, J., Lervik, A., Wijn, A. S.</p> <p># Content</p> <p>The folder "glycamstructures" contains the output from GLYCAM carbohydrate builder with force-field parameters, coordinates and topology of a single chain of cellulose composed of 4 cellobiose units.</p> <p>The folder "intermol" contains the output from the software Intermol were the content of "glycamstructures" is translated into the lingo of LAMMPS.</p> <p>The folder "packmol" contains input files for the software Packmol, which was used for creating coordinate-files for solvated systems of cellulose with water and cellulose with the solvent mixture. (The bash-script clean.sh fixes the formatting of the .xyz-files such that LAMMPS can read it.)</p> <p>The folder "36bundle" contains input files for creating the initial configuration of the bundle with 36 chains in a maze configuration, namely 36bundle.sh reads 1chain.xyz to make the coordinate file 36bundle.xyz. Further, nvt00.in is the initial LAMMPS script for slowly starting up this system with the NVT-ensemble, which reads system information from the LAMMPS-datafile chain.36bundle, cellulose parameters from the LAMMPS-datafile data.cellulose_nohybrid and coordinates from 36bundle.xyz. nvt01.in and nvt02.in continues sequentially, resulting in the configuration stored in the LAMMPS data-file chain.nvt02.</p> <p>The folder "36bundle_mix" continues from 36bundle by first adding the solvent mixture with coordinates from Packmol in the file 36bm.xyz and slowly starting up in the NVT-ensemble with nvt00.in, with force-field parameters in data.cellulose.in, data.water_spce.in, data.naoh.in and data.urea.in, with charges and connectivity of the solvent molecules in the files water_spce.txt, naoh.txt and urea.txt. The LAMMPS script npt00.in continues with the NPT-ensemble, which is continued with npt01.in etc., resulting in the configuration stored in the LAMMPS data-file chain.npt06. These systems can be continued with length- or force-controlled oscillatory stretching/compression with osc_length.in and osc_force.in respectively, which was used to make the configurations stored in the LAMMPS data-files chain.osc_length4 and chain.osc_force4.</p> <p>Similarly, the folder "36bundle_water" continues from 36bundle by first adding the water with coordinates from Packmol in the file 36bw.xyz and slowly starting up in the NVT-ensemble with nvt00.in, with force-field parameters in data.cellulose.in and data.water_spce.in, with charges and connectivity of the water molecules in the file water_spce.txt. Again, these systems can be continued with length- or force-controlled oscillatory stretching/compression with osc_length.in and osc_force.in respectively, which was used to make the configurations stored in the LAMMPS data-files chain.osc_length4 and chain.osc_force4.</p>
Spatiotemporal dissolution of calcite grain column packs
<p>The binarized reference data from time-lapse high-resolution X-ray CT imaging of live flow experiments and effluent pH and TDS logs are stored here. Data post-processing and analysis were conducted using a commercial software Avizo - details if which are described in the methods and results sections of the companion manuscript.</p>
Data from: Shell dissolution rates differ fourfold between mussel species
Open the record for dataset details and reuse information.
Extended Data: Structure-dependence of the atomic-scale mechanisms of Pt electrooxidation and dissolution
<p><strong>Abstract:</strong></p> <p>Platinum dissolution and restructuring due to surface oxidation are primary degradation mechanisms that limit the lifetime of Pt-based electrocatalysts for electrochemical energy conversion. Here, we studied well-defined Pt(100) and Pt(111) electrode surfaces by in situ high-energy surface X-ray diffraction, on-line inductively coupled plasma mass spectrometry, and density functional theory calculations, to elucidate the atomic-scale mechanisms of these processes. The locations of the extracted Pt atoms after Pt(100) oxidation reveal distinct differences from the Pt(111) case, which explains the different surface stability. The evolution of a specific stripe oxide structure on Pt(100) produces unstable surface atoms which are prone to dissolution and restructuring, leading to one order of magnitude higher dissolution rates.</p> <p><strong>Contents of this repository:</strong></p> <p><strong>1. SXRD data:</strong></p> <p>The experiments for the acquisition of the raw SXRD data were performed at the the European Synchrotron Radiation Facility, Grenoble, France at the beamlines ID31 and ID03. We thank H. Isern and T. Dufrane for the help during the SXRD experiments.</p> <ul> <li>tomo_tomo.spec is the file with the X-ray diffraction metadata for the CTR scans. It is a plain text file.</li> <li>Each HESXRD dataset is saved in a folder, which denotes the potential (e.g. 1V0.zip). The png-image files are previews of the corresponding dataset. The individual raw cbf files can be opened by pyMCA or silx. From python, the images can be accessed using the fabio library.</li> <li>calibration.zip contains the pyFAI calibration files and a list with indexed Bragg reflections for the UB matrix calculation</li> <li>CTRs_parameters.zip contains the averaged CTR structure factors used for the structual analysis as well as files with the atomic coordinates of the refined structural model.</li> <li>steps.zip contains the full datasets from the potential step experiments in Fig. 1e.</li> </ul> <p><strong>2. DFT:</strong></p> <ul> <li>CONTCARs.zip contains the atomic coordinates of the optimized computational models.</li> </ul> <p> </p> <p> </p>
Carbonate chemistry changes following iron and steel slags dissolution in seawater for Ocean Alkalinity Enhancement, and measured dissolution of potentially toxic elements.
<p>Ocean alkalinity enhancement is a carbon capture strategy that has gained interest over the past years. This strategy relies on the dissolution of alkaline minerals to increase the alkalinity of the ocean, among which iron and steel slags are potential candidates. However, their dissolution in seawater as well as the leaching of potentially toxic elements is unknown. These data were collected as part of a research article that assess the alkalinity generation potential of iron and steel slags in MilliQ and seawater, as well as the dissolution of potentially toxic elements. The dataset is composed of various sheets, each of them reporting data from a specific experiment. For each experiment, the analysis details (instrument used, parameters analysed etc) are provided on each individual sheet, and an overview one regroups the main aims of this research as well as the technical terms used throughout.</p>
Dataset for Formulation of minitablets with personalised dissolution profile by fluid-bed granulation of drug nanosuspensions
<p>Source data for all graphs in the manuscript titled "Formulation of minitablets with personalised dissolution profile by fluid-bed granulation of drug nanosuspensions" by Mutylo et al., submitted to International Journal of Pharmaceutics. The data sets contains values for particle size distributions, tablet uniformity, drug loading, and dissolution curves.</p>
Pore matrix dissolution in carbonates: An in-situ experimental investigation of carbonated water injection
<p>Carbonate rocks in underground formations are major targets for oil extraction and carbon storage. The solid part of these porous rocks contains certain minerals, such as calcite and dolomite, that can interact with aqueous solutions. Interactions could be reactive, which leads to the dissolution of these minerals. In this study, we investigated the evolution of carbonate rock dissolution during the flow of carbonated water in pores that initially contain both oil and brine. Carbonated water is an aqueous solution enriched with carbon dioxide (CO<sub>2</sub>); hence, it is acidic. In our experiments, we observed that the reactive flow and transport of carbonated water is characterized by two distinct periods. The first is a pre-dissolution period where the CO<sub>2</sub> molecules diffused from the flowing carbonated water into the oil causing it to swell. As separate oil globules swelled, they reconnected and moved in the direction of the flowing water toward the outlet of the rock sample. In the second stage, significant mineral dissolution occurred creating wormholes that had either a conical or a dominant pattern. The pattern and extent of dissolution was dependent on the flow rate of the carbonated water and its CO<sub>2</sub> concentration.</p>
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