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102 results for “Dissolution”
Laboratory Assessment of the Impact of Chemical Oxidation, Mineral Dissolution, and Heating on the Nitrogen Isotopic Composition of Fossil-bound Organic Matter
<p>Results of laboratory experiments reported in the manuscript "<em>Laboratory Assessment of the Impact of Chemical Oxidation, Mineral Dissolution, and Heating on the Nitrogen Isotopic Composition of Fossil-bound Organic Matter</em>", published in the journal <em>Geochemistry, Geophysics, Geosystems</em></p>
Effects of hydrostatic dissolution and seepage on the transport and mechanical properties of glauberite
<p>Data presented and discussed in the article entitled "Effects of hydrostatic dissolution and seepage on the transport and mechanical properties of glauberite".</p>
Dataset on the effects of mineral grain size and seawater salinity on Mg(OH)2 dissolution and CaCO3 precipitation kinetics.
<p>Dataset from the manuscript "Effects of grain size and seawater salinity on magnesium hydroxide dissolution and secondary calcium carbonate precipitation kinetics: implications for ocean alkalinity enhancement" from Moras et al., 2024 (https://doi.org/10.5194/egusphere-2024-645). The dataset compiles all data used in the manuscript. The manuscript covers Mg(OH)2 dissoluton and CaCO3 precipitation kinetics for Ocean Alkalinity Enhancement. These kinetics are reported under different conditions, such as varying grain size and seawater salinity.</p>
Modelling Amorphization and Dissolution of Zr(Fe,Cr)2 Secondary Phase Precipitates in Zircaloys
<p>Data and accompanying paper "Modelling Amorphization and Dissolution of Zr(Fe,Cr)2 Secondary Phase Precipitates in Zircaloys" J. D. Robson, Journal of Nuclear Materials</p>
Replication Package for: "The Impact of Cartel Dissolution on Prices: Evidence from the Air Cargo Cartel"
<div> <div> <div> <p>This replication package contains an online appendix, data, and code used to generate 4 figures and 2 tables in the main text of the paper "The Impact of Cartel Dissolution on Prices: Evidence from the Air Cargo Cartel" by Douglas C. Turner.</p> </div> </div> </div>
Analysis scripts and PFLOTRAN input files for "Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface"
<p>Supporting files for manuscript <em>Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface </em>(preprint published at https://arxiv.org/abs/2305.12575).</p> <p>Contents:</p> <ul> <li>PFLOTRAN input files (*.in) for the simulations that were used in the manuscript.</li> <li>The corresponding heterogeneous permeability fields for those simulations (*.h5).</li> <li>The CO2 property database for use with the PFLOTRAN MPHASE module.</li> <li>Python scripts for generation of the random fields and quantification of uptake rate.</li> <li>Jupyter notebooks for generation of summary figures.</li> <li>Excel spreadsheet summarizing the output of each of the simulations.</li> </ul>
Comparison of Daprodustat Formulations Produced by Two Methods of Manufacture for Bioequivalence and Dissolution in Healthy Participants
ClinicalTrials.gov study NCT04640311. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Flow heterogeneity controls dissolution dynamics in topologically complex rocks
Open the record for dataset details and reuse information.
Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel with dynamic changes in the scanning geometry
<p><strong>Summary</strong></p> <p>This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel.</p> <p>This is the second experiment detailed in Case Study 3 in [Coban 2020], and this submission can be treated as a follow up to [Coban&Lucka 2019]. </p> <p> </p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p> </p> <p><strong>Sample Information</strong></p> <p>The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.</p> <p> </p> <p><strong>Experimental Plan</strong></p> <p>This experiment is performed such that 150 projections are collected over 360 degrees, for a total of 166 rotations, with exposure time 12 ms for each projection. This means that in total the submission contains 25000 projections. This experiment took 5 minutes of acquisition time, during which we (at user's command) zoom in onto the bottom of the sample holder (i.e. where the tablet rests). We later (again, at user's command) shift the view (i.e. the tube and the detector) upwards to the top of the sample to observe foaming on the surface. Finally, before the end of the 5-minute acquisition period, we zoom out to the original magnification. Every time the geometry undergoes a major change such as zooming in (which would affect the reconstruction), the system creates a new data settings file with the new geometrical information, appended by the projection number, therefore marking the change. However, since there is no major change created by the vertical shift of the tube and detector (as in no change in geometry that would affect the reconstructed images), there is no new data settings file for this event. </p> <p>The spatial resolution for this data is 193μm at the beginning (or end) of the experiment, which at an arbitrary point changes to 76μm. For a smooth data transfer, each projection image is binned down to the size of 486px-by-384px. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed.</p> <p>All raw data (i.e. with no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission is given below.</p> <ul> <li><strong>scan_1</strong>: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif,</em></li> <li>pre flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>post flat-field (or open-shutter after acquisition) image, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em> (25000 projections in total),</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata (this is the final geometry info file),</li> <li><em>data settings XRE_5220.txt</em> (geometry info recorded after the zoom-in)</li> <li><em>data settings XRE__22610.txt</em> (geometry info recorded after the zoom-out, same as <em>data settings XRE.txt</em>)</li> </ul> </li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> </ul> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.</p>
Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel
<p><strong>Summary</strong></p> <p>This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel.</p> <p>The data is made available as part of Case Study 3 in [Coban 2020].</p> <p> </p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p> </p> <p><strong>Sample Information</strong></p> <p>The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.</p> <p> </p> <p><strong>Experimental Plan</strong></p> <p> </p> <p>This experiment is performed such that 120 projections are collected over 360 degrees, for a total of 83 rotations, with exposure time 30 ms for each projection. This means that in total the submission contains 10000 projections. This took a total of 5 minutes of acquisition time, during which the tablet moved due to saturation but did not completely dissolve. The spatial resolution is 95μm, and the field of view was cropped to the boundaries of the sample holder (each projection image is of size 647px×768px). Our experimental setup allowed the collection of open and closed shutter (flat- and dark-field) images before decanting the gel onto the tablet. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed.</p> <p>All raw data (i.e. no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission is given below.</p> <ul> <li><strong>scan</strong>: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif,</em></li> <li>pre flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>post flat-field (or open-shutter after acquisition) image, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em> (10000 projections in total),</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>script.txt</em> and <em>script_executed.txt</em> are the text files containing the list of commands the apparatus has executed.</li> </ul> </li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> </ul> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.</p> <p> </p>
Data from: Let's stay together? Intrinsic and extrinsic factors involved in pair bond dissolution in a recolonizing wolf population
For socially monogamous species, breeder bond dissolution has important consequences for population dynamics, but the extent to which extrinsic or intrinsic population factors causes pair dissolution remain poorly understood, especially among carnivores. Using an extensive life-history data set, a survival analysis and competing risks framework, we examined the fate of 153 different wolf (Canis lupus) pairs in the recolonizing Scandinavian wolf population, during 14 winters of snow tracking and DNA monitoring. Wolf pair dissolution was generally linked to a mortality event and was strongly affected by extrinsic (i.e. anthropogenic) causes. No divorce was observed, and among the pair dissolution where causes have been identified, death of one or both wolves was always involved. Median time from pair formation to pair dissolution was three consecutive winters (i.e. approximately 2 years). Pair dissolution was mostly human-related, primarily caused by legal control actions (36·7%), verified poaching (9·2%) and traffic-related causes (2·1%). Intrinsic factors, such as disease and age, accounted for only 7·7% of pair dissolutions. The remaining 44·3% of dissolution events were from unknown causes, but we argue that a large portion could be explained by an additional source of human-caused mortality, cryptic poaching. Extrinsic population factors, such as variables describing the geographical location of the pair, had a stronger effect on risk of pair dissolution compared to anthropogenic landscape characteristics. Population intrinsic factors, such as the inbreeding coefficient of the male pair member, had a negative effect on pair bond duration. The mechanism behind this result remains unknown, but might be explained by lower survival of inbred males or more complex inbreeding effects mediated by behaviour. Our study provides quantitative estimates of breeder bond duration in a social carnivore and highlights the effect of extrinsic (i.e. anthropogenic) and intrinsic factors (i.e. inbreeding) involved in wolf pair bond duration. Unlike the effects of intrinsic and extrinsic factors that are commonly reported on individual survival or population growth, here we provide quantitative estimates of their potential effect on the social unit of the population, the wolf pair.
Data for: Andic soil properties and tephra layers hamper C turnover in Icelandic peatlands - selective dissolution of Al, Fe, Si and decomposition proxies
<p>Due to frequent volcanic activity and erosion of dryland soils, peatlands in Iceland receive regular additions of mineral aeolian deposits (tephra and eroded material). Therefore, their soils may develop not only histic, but also andic characteristics. Here we present data sets of a study that elucidates interactions between carbon characteristics and andic soil properties in Histosols of three Icelandic peatlands. The data sets contain information about the soil carbon structure derived by <sup>13</sup>C NMR spectroscopy, andic soil properties based on selective dissolution of Al, Fe and Si, decomposition proxies C/N, δ<sup>13</sup>C and δ<sup>15</sup>N, information on total carbon and nitrogen content, dry bulk density, soil organic matter content and pH measured in deionised water and NaF solution.</p>
Abundance and fractional solubilities of aerosol metals in urban Hong Kong: Insights into factors that control aerosol metal dissolution in an urban site in South China
<p>Our manuscript was submitted to ACP. Upon submission, all data must be accessible by anonymous access.</p>
Raw data: Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides
<p>Raw data for the manuscript titled:</p> <p><strong>Unravelling the mechanistic complexity of oxygen evolution reaction and Ir dissolution in highly dimensional amorphous hydrous iridium oxides</strong></p> <p> </p>
(Data on) Regenerated cellulose-mineral composite films formed from dissolution in ionic liquid: comparing calcium carbonate and kaolinite particulate fillers
<p>This dataset covers outcomes of research performed by the authors at Aalto University during years 2023-2024. More indepth analysis of the results will be provided in a separate scientific publication.</p>
Flux of density-driven convective dissolution in heterogeneous porous media
<div> <p>Dissolution of CO2 in to brine can increase the density of CO2 rich brine and can cause density driven convection, which can enhance the dissolution of CO2 into brine. Permeability heterogeneity in natural saline aquifers cuases uncertaintis in dissolutio rates, which is measured with mass flux at the CO2-brine interface. Both laboratory experiments and numerical simiulations were conducted to investigate the impact of permeability heterogeneity on convective dissolution. This dataset includes the flux at the CO2-brine interface against time of 48 experiments and 2000 numerical simulations.</p> <p> </p> <p> </p> </div>
Data for the paper, Origin of copper dissolution in electrocatalytic reduction conditions involving amines
<p>This dataset supports the paper titled "Origin of copper dissolution in electrocatalytic reduction conditions involving amines" The study aims to elucidate the atomic-scale mechanism of copper (Cu) dissolution under electrochemical conditions, particularly in the presence of acetone and methylamine mixtures, and under reductive potential conditions (-0.75V vs RHE). </p> <p><strong>Usage:</strong> This dataset is valuable for researchers focusing on electrochemical amination, cathode degradation processes, and the design of durable Cu-based cathodes. The insights from the GC-DFT calculations provide a fundamental understanding of Cu dissolution facilitated by surface restructuring in amine solutions under electroreduction conditions. The data can be used to further explore the mechanisms of Cu electrode corrosion and to design more stable and efficient electrochemical systems involving amines.</p>
Towards the conservation of Brazilian legumes: Summary, land cover and use analytics, and a comparative analysis of locations count methods (curated vs. automated [buffer-dissolution])
<p>This dataset presents key findings from the study "<strong>Automating and Enhancing Species Extinction Risk Assessments with Historical Land Use and Land Cover Data</strong>."</p> <p>The file `<em>summary-threatened-legume-species.ods</em>` provides a summary of all threatened species of the Leguminosae family native to Brazil, including IUCN category and criteria, location counts, and the year of the latest assessment, sourced from official records. Additionally, it includes calculated values for Area of Occupancy (AOO) and Extent of Occurrence (EOO), along with trend data indicating natural area change rates (decline [positive number, red] or growth [negative number, green]) within both AOO and EOO. Location counts are further detailed across various buffer radii (1-5 km), utilizing a buffer-dissolution method for automated location counting. Each buffer radius is analyzed to assess AOO and EOO decline, where '1' indicates a species is threatened and '0' indicates it is not. This data provides an efficient method for screening threatened species under criterion B of the IUCN Red List guidelines.</p> <p>The file `<em>overlay-analysis.ods</em>` contains overlay analysis results for AOO and EOO of each species using MapBiomas land use and land cover (LULC) data (specifically MapBiomas Brazil, collection 7.1) from 1985 to 2021, covering all threatened legume species. This file provides both absolute area in square kilometers and percentages for each LULC class. The overlay analysis results support estimates of growth and decline trends for each LULC class.</p> <p>The file `<em>trend-analysis.ods</em>` presents results of annual rate estimates from trend analysis across LULC classes, and including both natural and anthropic groupings. A complete JSON database with p-values and R² values is provided in `<em>trend-analysis.json</em>`.</p> <p>This approach, combining all results for each species in a comprehensive, merged dataset, allows for effective filtering and ranking of the most threatened species as well as identification of their primary threats.</p> <p>We recommend opening the ODS files with LibreOffice, as Microsoft Excel may experience issues parsing decimal formats accurately.</p> <p>More detailed maps and graphs are available at <a title="LULC-MapBiomas-Leguminosae" href="https://github.com/lsbjordao/LULC-MapBiomas-Leguminosae" target="_blank" rel="noopener">https://github.com/lsbjordao/LULC-MapBiomas-Leguminosae</a>.</p>
Data from: Degradation of internal organic matter is the main control on pteropod shell dissolution after death
The potential for preservation of thecosome pteropods is thought to be largely governed by the chemical stability of their delicate aragonitic shells in seawater. However, sediment trap studies have found that significant carbonate dissolution can occur above the carbonate saturation horizon. Here we present the results from experiments conducted on two cruises to the Scotia Sea to directly test whether the breakdown of the organic pteropod body influences shell dissolution. We find that, on the timescales of three to thirteen days, the oxidation of organic matter within the shells of dead pteropods is a stronger driver of shell dissolution than the saturation state of seawater. Three to four days after death, shells became milky white and nano‐SEM images reveal smoothing of internal surface features and increased shell porosity, both indicative of aragonite dissolution. These findings have implications for the interpretation of the condition of pteropod shells from sediment traps and the fossil record, as well as for understanding the processes controlling particulate carbonate export from the surface ocean.
Supplementary Data for "Hydrate Formation on Marine Seep Bubbles and the Implications for Water Column Methane Dissolution"
<p>This data deposit contains the data necessary to reproduce results presented in the paper "Hydrate Formation on Marine Seep Bubbles and the Implications for Water Column Methane Dissolution", to appear in JGR Oceans. </p> <p>The files "Fu_AGUSupplementary_S11.xlsx" and "Fu_AGUSupplementary_S12.xlsx" are referenced in the supplement of the original paper. </p> <p>The file "Fu_AGUSupplementary_additionalDATA.xlsx" contains all the data and inputs used to create Figure 14, 15 and 16 of the original manuscript.</p>
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