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Dataset results
251 results for “Electrolyte”
Validation of the Accuracy of a Novel POCT Dry Electrolyte Analysis System in the Acute Care Setting
ClinicalTrials.gov study NCT06726460. IPD Sharing: YES. Countries: 1. Publications: 15.
A Study to Investigate the Mechanistic Effects of Dapagliflozin Alone or in Combination With Balcinrenone, Compared to Balcinrenone and Placebo on Body Fluid and Electrolyte Handling and Energy Metabo
ClinicalTrials.gov study NCT05884866. IPD Sharing: YES. Countries: 2. Publications: 4.
Variation in Urine Electrolytes, pH and Specific Gravity Throughout the Day
ClinicalTrials.gov study NCT03645785. IPD Sharing: NO. Countries: 1. Publications: 21.
Heart-Lung Machine: Impact of the Priming Solution on Acid-Base Balance, Electrolytes and Outcome on Patients Undergoing Cardiac Surgery
ClinicalTrials.gov study NCT07267546. IPD Sharing: YES. Countries: 1. Publications: 4.
Improved Mechanical Strength without Sacrificing Li-Ion Transport in Polymer Electrolytes
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Data from: Frost damage measured by electrolyte leakage in subarctic bryophytes increases with climate warming
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Influence of water sorption on ionic conductivity in polyether electrolytes at low hydration
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Data used for the paper Andres Parra-Puerto, Kai Ling NG, Kieran Fahy, Angela E Goode, Mary P. Ryan, and Anthony Kucernak Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER and Corrosion Resistance in Acid and Alkaline Electrolytes ACS Catalysis, 2019 DOI: 10.1021/acscatal.9b03359
<p>This is an updated version of the spreadsheet which corrects the first version which had the wrong datasets provided for figure 2 c and d (ORR) </p> <p> </p> <p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Andres Parra-Puerto, Kai Ling NG, Kieran Fahy, Angela E Goode, Mary P. Ryan, and Anthony Kucernak</p> <p>Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER and Corrosion Resistance in Acid and Alkaline Electrolytes</p> <p>ACS Catalysis, 2019</p> <p>DOI: 10.1021/acscatal.9b03359Please cite the above reference if you wish to use this data</p>
Dataset for "Strain induced electrochemical behaviours of ionic liquid electrolytes in an electric double layer capacitor: Insights from molecular dynamics simulations"
<p>The datafile contains molecular dynamics simulation results for analysing the electrochemical behaviour of ionic liquid based EDLC under compression and tension.</p>
Molecular dynamics trajectories for ionic conductors in: Paradigms of frustration in superionic solid electrolytes
<p>Superionic solid electrolytes have widespread use in energy devices, but the fundamental motivations for fast ion conduction are often elusive. Here, we draw upon atomistic simulations of a wide range of halide, oxide, sulfide, and <em>closo</em>-borate superionic conductors to illustrate some of the key features that enhance local cation mobility in these solids. We classify three types of frustration that create competition between different local atomic preferences, thereby flattening the diffusive energy landscape and enhancing entropy. These include chemical frustration, which derives from competing factors in the anion-cation interaction; structural frustration, which is connected to the lack of a clear site preference for mobile ion ordering; and dynamical frustration, which is associated with temporary fluctuations in the energy landscape due to anion orientations or cation reconfigurations. For each class of frustration, we provide detailed simulation analyses of multiple materials to show how ion mobility is facilitated, resulting in stabilizing factors that are both entropic and enthalpic in origin. Implications for identifying suitable descriptors for superionic conductivity are discussed.</p>
Foliar summer frost resistance measured via electrolyte leakage approach as related to plant distribution, community composition and performance
1. Frost resistance (FR) is a fundamental process determining various aspects of plant life. Measurements of FR remain challenging as existing methods are time- and labour-intensive. 2. Here, we test the applicability of foliar summer FR measured via the 'electrolyte leakage' approach (FR<sub>PEL</sub>), a simple and inexpensive technique so far underused in ecological research to estimate FR. We tested the ability of this trait to predict i) species occurrence, ii) community composition along an elevational gradient, and iii) analysed its relationship to 9 other traits (SLA, C<sub>mass</sub>, N<sub>mass</sub>, P<sub>mass</sub>, Ca<sub>mass</sub>, Mg<sub>mass</sub>, K<sub>mass</sub>, canopy height and the ability to form rosettes) related to plant performance. We studied 183 vascular species occurring at 37 sites, which we also analysed in terms of species composition along an elevation gradient from 656 to 2363 m a.s.l. in the Bavarian Alps, Germany. 3. Species' FR<sub>PEL</sub> values correlated significantly with the species occurrence along the elevational gradient. However, this relationship was weak as it explained only 10% in the variation of the Landolt indicator values, a proxy for species geographic ranges. We found a strong positive relationship between community-weighted FR<sub>PEL</sub> values and elevation suggesting a strong environmental filtering of this trait that removes species with low FR from the local species pool at high elevations. The community functional diversity of FR<sub>PEL</sub> significantly increased with increasing elevation suggesting a higher trait divergence in harsher climates. We found significant negative relationships between FR<sub>PEL</sub> and specific leaf area, canopy height and the ability to form leaf rosettes, indicating a trade-off in plants between the investment in conservative strategies, in our case FR, and fast resource acquisition. 4. Our study highlights the potential of using FR<sub>PEL</sub> more frequently in ecological research as we were able to re-confirm three patterns inferred by more precise yet time-consuming and labour-intensive approaches. We demonstrate that FR<sub>PEL</sub> can be used to predict species occurrence and infer assembly rules along elevational gradients. Furthermore, we found that FR<sub>PEL</sub> is linked to plant functional traits associated with plant performance and morphological features of the plants. However, the trait's predictive power varied greatly among the study objectives and levels.
Data set for theh paper "Recovery of Polymer Electrolyte Fuel Cell exposed to sulfur dioxide" DOI:10.1016/j.ijhydene.2016.01.077
<p>This Excel file contains the data from the following publication</p> <p><br /> Biraj Kumar Kakatia, Anusree Unnikrishnan, Natarajan Rajalakshmi, RI Jafri, KS Dhathathreyan, Anthony RJ Kucernak</p> <p>Recovery of Polymer Electrolyte Fuel Cell exposed to sulfur dioxideInternational Journal of Hydrogen Energy</p> <p>2016</p> <p>DOI:10.1016/j.ijhydene.2016.01.077</p>
Dataset for paper "Investigation of convective transport in the so-called 'gas diffusion layer' used in polymer electrolyte fuel cell"
<p>Dataset containing all data for figures and supplemental material for the paper:<br /> Investigation of convective transport in the porous media of a fuel cell-like system</p> <p>O. Beruski, T. Lopes, A. R. Kucernak and J. Perez.</p>
Data file for the paper "Using corrosion-like processes to remove poisons from electrocatalysts: a viable strategy to chemically regenerate irreversibly poisoned polymer electrolyte fuel cells", Electrochimica Acta 2016, DOI: 10.1016/j.electacta.2016.11.054
<p>Data used in producing the figures in the paper described below</p> <p>If you use this data then please specify as a reference</p> <p>B. K. Kakati, A. R. J. Kucernak, and K Fahy, "Using corrosion-like processes to remove poisons from electrocatalysts: a viable strategy to chemically regenerate irreversibly poisoned polymer electrolyte fuel cells "Electrochimica ActaDOI: 10.1016/j.electacta.2016.11.054</p> <p>Supported by funding from the Engineering and Physical Sciences Research Council under project EP/I037024/1 and the Technology Strategy board under the IDP11 framework for project 102283. </p>
Potentiodynamic polarisation curves and pitting descriptors of 316L stainless steel in NaCl electrolyte
<div>This deposit contains all data and code supporting the analysis in:</div> <div> </div> <div>Identifying stable pitting pathways in 316L stainless steel via fractal-inspired PCA-based clustering</div> <div>Coelho L.B., Amand T., Torres D., Olivier M., Ustarroz J. (accepted 21 April 2025, npj Materials Degradation)</div> <div> </div> <div>Included files</div> <div> </div> <div>calculated_epit_data.csv – the ML-estimated critical pitting potentials (Epit)</div> <div> </div> <div>calculated_logipit_data.csv – the ML-estimated log(jₚᵢₜ) values corresponding to Epit</div> <div> </div> <div>calculated_epass_data.csv – the ML-estimated passive potentials (Epass) and log(jₚₐₛₛ)</div> <div> </div> <div>calculated_epit_df_meta_data.csv – manually curated Epit values just before stable pit growth</div> <div> </div> <div>calculated_logipit_df_meta_data.csv – manually curated log(jₚᵢₜ) values just before stable pit growth</div> <div> </div> <div>Critical-descriptor definitions</div> <div> </div> <div>The critical pitting potentials (Epit) and passive potentials (Epass) were estimated using the machine learning (ML) model described previously [1].</div> <div>The files calculated_epit_df_meta_data.csv and calculated_logipit_df_meta_data.csv contain the last metastable-pitting descriptors—manually identified—immediately prior to the onset of stable pit growth.</div> <div> </div> <div>Experimental methods</div> <div>The macro-scale potentiodynamic polarization (PP) tests were performed at varying NaCl concentrations (0.005 M, 0.01 M and 0.05 M). Using an SP-300 (Bio-Logic) potentiostat inside a Faraday cage, the cell comprised:</div> <div> </div> <div>WE: 316L SS specimen (~1 cm² exposed area)</div> <div> </div> <div>RE: Ag/AgCl/KCl_sat inside a Luggin capillary</div> <div> </div> <div>CE: platinum foil</div> <div> </div> <div>After 60 min immersion to stabilize the open-circuit potential (OCP), polarization scans ran from –30 mV to +900 mV vs. OCP at 0.5 mV/s, matching our previous macro PP study on the same sample [2]. Each NaCl concentration was tested 28 times (84 curves total).</div> <div> </div> <div>References</div> <div> </div> <div>[1] L.B. Coelho, D. Torres, V. Vangrunderbeek, M. Bernal, G.M. Paldino, G. Bontempi, J. Ustarroz, Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis, Npj Mater Degrad 7 (2023) 82. https://doi.org/10.1038/s41529-023-00403-z.</div> <div> </div> <div>[2] L. B. Coelho, S. Kossman, A. Mejias, X. Noirfalise, A. Montagne, A. Van Gorp, M. Poorteman, M. G. Olivier, “Mechanical and corrosion characterization of industrially treated 316L stainless steel surfaces,” Surf. Coat. Technol. 382 (2020) 125175. https://doi.org/10.1016/j.surfcoat.2019.125175</div> <div> </div> <div> </div>
Datasets to Impact of Nano-sized Inorganic Fillers on PEO-based Electrolytes for Potassium Batteries
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Impact of Nano‐Sized Inorganic Fillers on PEO‐Based Electrolytes for Potassium Batteries"</strong></p><p>published in Batteries & Supercaps, <strong>2023</strong>, e202300404. https://doi.org/10.1002/batt.202300404</p><p> </p><p>Specifically, the following measurements are provided in separate zip folders:</p><p>Solid polymer electrolytes characterization:</p><p>Differential Scanning Calorimetry ("DSC.zip")</p><p>Rheological measurements ("Rheology.zip")</p><p>Electrochemical Impedance Spectroscopy ("PEIS.zip")</p><p>Plating and Stripping Experiments ("Plating-Stripping.zip")</p><p>Galvanostatic Cycling with Potential Limitations (GCPL.zip)</p><p> </p><p>Experimental and sample details, including assignment to filenames are provided in the respective README files.</p>
Supplemental Data for Cell Reports Physical Science article "Probing transference and field-induced polymer velocity in block copolymer electrolytes"
<p>The data and Jupyter notebook uploaded here is Supplemental Information for the article:</p><p><strong>Probing transference and field-induced polymer velocity in block copolymer electrolytes </strong></p><p>Coauthored by:</p><p>Michael D. Galluzzo, Hans-Georg Steinrück, Christopher J. Takacs, Aashutosh Mistry, Lorena S. Grundy, Chuntian Cao, Suresh Narayanan, Eric M. Dufresne, Qingteng Zhang, Venkat Srinivasan, Michael F. Toney, and Nitash P. Balsara.</p><p>Journal: Cell Reports Physical Science</p><p>Notes:</p><ul><li>This depository includes the experimental data used in Figure 2, 3, and 4 of the main text and an additional data set.</li><li>The Jupyter notebook "velocity_Echem_Data.ipynb' can be used to visualize the data in the .csv files provided in the folder 'echem' and 'XPCS_fits'.</li><li>The folder 'echem' contains the raw electrochemical data obtained from the two XPCS experiments discussed in the main text and an additional experiment set.</li><li>The folder 'XPCS_fits' contains the results of fitting the autocorrelation functions at each spatial position in the cell at each time point for the two XPCS experiments discussed in the main text and an additional experiment set. </li><li>The additional experiment included here (reffered to as Cell P in the Jupyter notebook) is not discussed in the main text, however it demonstrates that the second 'hump' in velocity (see Figure 3 and S5) that is observed after switching the direction of polarization was replicated in a separate experiment.</li></ul><p> </p>
Dataset for the paper "Nanostructured Catalyst Layer Allowing Production of Ultralow Loading Electrodes for Polymer Electrolyte Membrane Fuel Cells with Superior Performance" published in ACS Appl. Energy Mater.
<p>The data in this spreadsheet was used to produce the figures in the paper</p><p>Authors:</p><p>Colleen Jackson, Michalis Metaxas, Jack Dawson, Anthony Kucernak</p><p>Title:</p><p>Nanostructured Catalyst Layer Allowing Production of Ultralow Loading Electrodes for Polymer Electrolyte Membrane Fuel Cells with Superior Performance</p><p>Journal:</p><p>ACS Appl. Energy Mater. </p><p>DOI:</p><p>10.1021/acsaem.3c01987</p><p>Please cite the above reference if you wish to use this data</p><p>DOI of data:</p><p>10.5281/zenodo.10256698</p>
Figures for Development, retainment, and assessment of the graphite-electrolyte interphase in Li-ion batteries regarding the functionality of SEI-forming additives
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Dataset for publication: "Long term porosity of solid electrolyte interphase on model silicon anodes with liquid battery electrolytes"
<p>This is the dataset used to produce the figures for the publication "Long term porosity of solid electrolyte interphase on model silicon anodes with liquid battery electrolytes"</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.