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1,491 results for “Ione”
Data for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest"
<p>This is the dataset that has been analyzed for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest". Please contact the author (chao.yan@helsinki.fi) for more details. </p>
Data and Code for: Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations
<p>This repository contains the datasets and scripts used to obtain the Figures of the paper "Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations".</p> <p>The repository is organized as follows:</p> <p>- a) ISR model script</p> <p>- b) Script to visualize the two solutions of the TICA problem</p> <p>- c) Generator of uniformly selected parameters</p> <p>- d) Script to represent the space of parameters</p> <p>- Part 0) Monte Carlo simulation codes</p> <p>- Part I) Analysis of the efffect of the uncertainty on the initial guess</p> <p>- Part II) Analysis of the impact of the addition of parameters from the Plasma Line</p> <p>- Part III) Analysis of the information provided by the addition of each parameter</p> <p>- Part IV) Analysis of the effect of the uncertainty on the parameters added</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for ionization energies) results discussed in the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes.</p> <p>In each archive file there is a README explaining how to use the bundled scripts to process the data.</p>
Occurrence characteristics of electromagnetic ion cyclotron waves at Maitri during solar cycle 24
<p>This data is of Induction Coil Magnetometer installed at the Indian Antarctic station- Maitri (geographic 70.7oS, 11.8oE, L = 5). It belongs to the Indian Institute of Geomagnetism, Navi Mumbai. The data is from January 2011 to December 2017. It contains the information about the EMIC wave ground observation at L~5.</p>
Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results
<p><strong>Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results </strong></p> <p> </p> <p>This archive contains the data file for the five CPIC simulations run for paper Larsen et al 2019. [1, 2]. The data files contents and format are described.</p> <p> </p> <p>Each data file is stored in HDF5 format written with h5py [3, 4].</p> <p>The files named field_potential_bias_XX_data.h5 contain the electric field components and electric potential with the following structure:</p> <blockquote> <p>+</p> <p>:|____Author (str [50])</p> <p>:|____Bias (str [3])</p> <p>:|____DOI (str [22])</p> <p>:|____Date (str [14])</p> <p>:|____Description (str [81])</p> <p>:|____File_Creator (str [44])</p> <p>:|____License (str [2140])</p> <p>|____E (h5py._hl.dataset.Dataset (3, 101, 101, 101))</p> <p> :|____Description (str [41])</p> <p> :|____Format (str [94])</p> <p> :|____Units (str [3])</p> <p>|____XYZ (h5py._hl.dataset.Dataset (3, 101))</p> <p>|____phi (h5py._hl.dataset.Dataset (101, 101, 101))</p> <p> </p> </blockquote> <p>See the metadata within the file for units of each variable.</p> <p> </p> <p>The files named detector_bias_XX_data.h5 contain the particles collected at the simulated detector in a mix of physical and CPIC units with the following structure:</p> <blockquote> <pre>+</pre> <pre>:|____Author (str [50])</pre> <pre>:|____Bias (str [4])</pre> <pre>:|____DIO (str [22])</pre> <pre>:|____Date (str [14])</pre> <pre>:|____Description (str [81])</pre> <pre>:|____Detector_area (str [11])</pre> <pre>:|____File_Creator (str [44])</pre> <pre>:|____License (str [2140])</pre> <pre>:|____Timestep (str [12])</pre> <pre>:|____Timestep_description (str [114])</pre> <pre>:|____electron_temperature (str [6])</pre> <pre>:|____photoemission_electron_energy (str [4])</pre> <pre>:|____potential_at_boundary (str [3])</pre> <pre>:|____proton_electron_mass_ratio (str [4])</pre> <pre>:|____proton_temperature (str [7])</pre> <pre>:|____reference_density (str [9])</pre> <pre>|____Energy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [32])</pre> <pre> :|____Units (str [2])</pre> <pre>|____Velocity (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Velocity_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vx (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vx_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vy_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vz (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vz_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____phi (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre> :|____Units (str [3])</pre> <pre>|____q (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [47])</pre> <pre> :|____Units (str [4])</pre> <pre>|____species (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [26])</pre> <pre>|____theta (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [37])</pre> <pre> :|____Units (str [3])</pre> <pre>|____timestep (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre>|____weight (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [38])</pre> <pre> :|____Units (str [4])</pre> </blockquote> <p> </p> <p>The information in parentheses are the datatype and size, square brackets, [], denote a single element of that many characters and parentheses, (), denote the stored array size. Lines with a leading colon, :, are metadata fields to make the data files more usable.</p> <p> </p> <p>1. Delzanno, G.L., et al., <em>CPIC: a curvilinear particle-in-cell code for plasma–material interaction studies.</em> IEEE Transactions on Plasma Science, 2013. <strong>41</strong>(12): p. 3577-3587.</p> <p>2. Meierbachtol, C.S., et al., <em>An electrostatic Particle-In-Cell code on multi-block structured meshes.</em> Journal of Computational Physics, 2017. <strong>350</strong>: p. 796-823.</p> <p>3. Folk, M., A. Cheng, and K. Yates. <em>HDF5: A file format and I/O library for high performance computing applications</em>. in <em>Proceedings of supercomputing</em>. 1999.</p> <p>4. Collette, A., et al., <em>h5py/h5py 2.9.0.</em> 2018.</p> <p> </p> <p> </p>
X-ray and ion irradiation effects on azurite, malachite and alizarin pictorial models
<p>XPS was used to analyze X-ray effects on pellets of azurite, malachite and alizarin pure pigments and on azurite and alizarin tempera paint mock-ups, where the pigments were mixed with egg yolk. X-ray radiation damage was assessed by comparing successive high-resolution spectra acquired on the same spot. The spectra of the pure pigments show low radiation sensitivity and reactivity under the selected X-ray irradiation conditions, although some differences among them were detected. Whereas alizarin does not experience surface chemical changes during the prolonged irradiation experiment, a slow progressive chemical reduction of azurite and malachite (copper-based pigments), together with the formation of cuprite, is observed. On the other side, tempera paint mock-ups are sensitive to X-rays and alterations of the egg yolk binding medium are displayed in a shorter time scale than the induced chemical modifications of the pure pigments. Besides, XPS allows to determine that the azurite pigment and the binder interact chemically. Azurite is partially reduced in the tempera paint from the beginning of the irradiation experiment and the subsequent X-ray exposure induces further reduction.</p>
X-ray and ion irradiation effects on azurite, malachite and alizarin pictorial samples
<p>XPS was used to analyze X-ray effects on pellets of azurite, malachite and alizarin pure pigments and on azurite and alizarin tempera paint mock-ups, where the pigments were mixed with egg yolk. X-ray radiation damage was assessed by comparing successive high-resolution spectra acquired on the same spot. The spectra of the pure pigments show low radiation sensitivity and reactivity under the selected X-ray irradiation conditions, although some differences among them were detected. Whereas alizarin does not experience surface chemical changes during the prolonged irradiation experiment, a slow progressive chemical reduction of azurite and malachite (copper-based pigments), together with the formation of cuprite, is observed. On the other side, tempera paint mock-ups are sensitive to X-rays and alterations of the egg yolk binding medium are displayed in a shorter time scale than the induced chemical modifications of the pure pigments. Besides, XPS allows to determine that the azurite pigment and the binder interact chemically. Azurite is partially reduced in the tempera paint from the beginning of the irradiation experiment and the subsequent X-ray exposure induces further reduction</p>
Data set of "Large superconducting diode effect in ion-beam patterned Sn-based superconductor nanowire/topological Dirac semimetal planar heterostructures"
<p><span>Superconductor/topological material heterostructures are intensively studied as a platform for topological superconductivity and Majorana </span><span>physics</span><span>. However, the high cost of nanofabrication and the difficulty of preparing high-quality interfaces between the two dissimilar materials are common obstacles that hinder the observation of intrinsic physics and </span><span>the </span><span>realisation of scalable topological devices and circuits. </span><span>Here, we demonstrate an innovative method to directly draw nanoscale superconducting </span><span><span>beta-tin (</span></span><span><span>β-Sn</span></span><span><span>)</span></span><span><span> patterns of any shape in the plane of a topological Dirac </span></span><span><span>semimetal</span></span><span><span> (TDS) </span></span><span><span>alpha-tin (</span></span><span><span>α-Sn</span></span><span><span>)</span></span><span><span> thin </span></span><span><span>film</span></span><span><span> by irradiating a focused ion beam (FIB</span></span><span><span>). We utilise</span></span><span><span> the property that α-Sn undergoes a phase transition to superconducting β-Sn upon heating by FIB. </span></span><span><span>In β-Sn nanowires embedded in a TDS α-Sn thin film, we observe large </span></span><span><span>non-reciprocal</span></span><span><span> superconducting transport, where the critical current changes by 69% upon reversing the current direction. The superconducting diode rectification ratio <em>η</em> reaches a maximum of 35% when the magnetic field is applied parallel to the current, </span></span><span><span>distinguishing</span></span><span><span> itself from all the previous reports</span></span><span><span>. Moreover, it</span></span><span><span> oscillates between alternate signs with increasing magnetic field strength. </span></span><span><span>The angular</span></span><span><span> dependence of <em>η</em> on the magnetic field and current directions is similar to that of the chiral anomaly effect in TDS α-Sn, suggesting that the </span></span><span><span>SDE</span></span><span><span> may occur at the α-Sn/</span></span><span><span>β</span></span><span><span>-Sn interfaces where the TDS α-Sn becomes superconducting by a proximity effect.</span></span><span><span> As superconducting TDSs are expected candidates for topological superconductivity and harboring Majorana bound states,</span></span><span><span> t</span></span><span><span>he ion-beam patterned Sn-based superconductor/TDS planar structures thus </span></span><span><span>show promise</span></span><span><span> as a universal platform for investigating novel quantum physics and devices based on topological superconducting circuits of any shape.</span></span></p>
Data for Partial Volume Deflagration Experiments with Synthesized Lithium-Ion Battery Thermal Runaway Effluent Gas.
<p>Refer to Readme.pdf or Readme.md for information on this dataset.<br><br>v1.1 - cleanup<br>v1.2 - readme updates</p>
Data for the publication "MgB2Se4 Spinels (B = Sc, Y, Er, Tm) as Potential Mg-Ion Solid Electrolytes – Partial Ionic Conductivity and the Ion Migration Barrier"
<h1><strong>Description Datasets</strong></h1> <p>Datasets are obtained from X-ray diffraction (XRD), Rietveld analysis, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), nuclear magnetic resonance (NMR) spectroscopy, electrochemical impedance spectroscopy (EIS), chronoamperometry (CA), chronopotentiometry (CP) and linear sweep voltammetry (LSV).</p> <h1><strong>Abstract</strong></h1> <p>The magnesium chalcogenide spinel MgSc~2~Se~4~ with high Mg-ion room-temperature conductivity has recently attracted interest as solid electrolyte for magnesium ion batteries. Its ionic/electronic mixed-conducting nature and the influence of the spinel composition on the conductivity and Mg^2+^ migration barrier are yet not well understood. Here, results from a combined experimental and computational study on four MgB~2~Se~4~ spinels (B = Sc, Y, Er, Tm) are presented. The room-temperature ionic conductivities (<em>σ</em>~ion~ = 2x10^–5^–7x10^–5^ S cm^–1^) of the spinels are accurately measured, as electron transport is effectively suppressed by purely Mg-ion conducting electrode interlayers. Using the same approach, reversible Mg plating/stripping as well as good electrochemical stability are achieved. Driven by the good accordance of the computationally and experimentally obtained Mg^2+^ migration barriers <em>E</em>~a~(th) and <em>E</em>~a~, respectively, further periodic density functional calculations are performed on the MgB~2~Se~4~ spinel system, revealing the role of trigonal distortion on the migration path geometry and <em>E</em>~a~(th). These findings provide deeper understanding how to reach small Mg^2+^ migration barriers <em>E</em>~a~ in the MgB~2~Se~4~ spinels.</p>
Distinct association of HRAS and KRAS with Mn(II) ion illustrated by paramagnetic NMR
<p><a name="OLE_LINK4"></a><span>RAS proteins are among crucial oncogenic proteins and are involved in several essential intracellular processes. The RAS protein has an intrinsic metal binding site for Mg<sup>2+</sup>, which is important for the conformational stability of the active site. Recently, it was reported that a second metal ion binding site, located further from the active site in HRAS, binds Ca<sup>2+</sup> with millimolar affinity.</span><span> As one of the most abundant metal ions in cells, Mn<sup>2+</sup> is a potential candidate for the second metal ion binding site in RAS proteins. Here, we examined the interaction of Mn<sup>2+</sup> with HRAS and KRAS using high resolution NMR spectroscopy. The NMR data showed that both the second metal ion binding site and the switch I and II regions bind Mn<sup>2+</sup> in the RAS proteins. Furthermore, our paramagnetic NMR results disclosed the conformational differences in helix </span><span>a</span><span>3 and the following loop between HRAS and KRAS, accompanied by the association with metal ion binding. These results provide new insights into the interaction of RAS proteins and Mn<sup>2+</sup> in the respective biological processes in cells.</span></p>
Dataset and code: Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges
<p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges. Joule (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p>
UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow
<p>UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. </p> <p>All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".</p> <p>This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.</p> <p>1. pre-processed training, evaluation and testing data for machine learning;</p> <p> UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt</p> <p>2. Streamlined input datasets based on the fragmentation dictionary;</p> <p> Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt</p> <p>3. Predictions on the validation and test sets;</p> <p> UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt</p> <p>4. Evaluation by comparison with Prosit;</p> <p> a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt</p> <p> b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt</p> <p>5. CSS for Different HCD Fragment Ion Series;</p> <p> CS_for_ion_splits.tsv</p> <p>6. Application 1: PSM rescoring;</p> <p> PSM rescoring_zipfiles.7z, PSM rescoring_readme.txt</p> <p>7. Application 2: In-silico spectral library search </p> <p> in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt</p> <p> </p>
Lithium-Ion Battery Field Data: 28 LFP battery systems with 8 cells in series, up to 5 years of operation
<div> <div> <div>This data set contains data from 28 portable 24V lithium iron phosphate (LFP) battery systems with approximately 160Ah nominal capacity. Each system's specific use case is unknown, but battery systems of this size are typically used as power sources for recreational vehicles, solar energy storage, and more.</div> <br> <div>All battery systems in this data set showed some form of unsatisfactory behavior and were returned to the manufacturer. Many reasons can cause a consumer to return a battery to the manufacturer for maintenance. The user's individual decisions may be motivated by personal judgment, BMS warnings, or customer support advice. This data set comprises a very small fraction of batteries sold of this version. Therefore, this data set is biased and not representative of the operational data of the entire population of this system version. An improved version replaced this battery system type. The battery system manufacturer provided the data set for this study and allowed its open-source release under the condition of anonymity.</div> <br> <div>Each battery system consists of 8 prismatic cells in series. Each system has one load current sensor, and each cell has one voltage sensor. The four temperature sensors are placed between adjacent cells, i.e., each temperature sensor is shared by two cells. Furthermore, the battery systems have active cell balancing. The available measurements vary from a single month to five years. Consequently, the number of data rows per system varies from several thousand to millions, depending on the duration of battery operation. The data set contains a total of 133 million rows of measurements.</div> </div> </div> <div> </div> <div> <div><strong>Associated Python Library</strong></div> <div>BattGP <a href="https://github.com/JoachimSchaeffer/BattGP" target="_blank" rel="noopener">https://github.com/JoachimSchaeffer/BattGP</a></div> <div>This library contains classes and functions to analyze the data set with Gaussian processes.</div> <div>Furthermore, data visualization functions are part of the library.</div> <div><br><strong>Associated Article<br></strong>Gaussian Process-based Online Health Monitoring and Fault Analysis of Lithium-Ion Battery Systems from Field Data</div> <div>Cell Report Physical Science <br> <div><a href="https://doi.org/10.1016/j.xcrp.2024.102258">https://doi.org/10.1016/j.xcrp.2024.102258</a></div> <div> </div> <div> </div> </div> </div>
Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Interfacial structurization between triolein and water from pH and buffer ions
Open the record for dataset details and reuse information.
Dataset for "On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles"
<p>Data for Kulmala et al. (2024 )"On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles" (https://doi.org/10.5194/ar-2024-14).</p> <p>Included in the file are number concentrations of sub-2 nm ions and 2-2.3 nm ions measured with Cluster Ion Counter (CIC) and Neutral cluster and Air Ion Spectrometer (NAIS) at SMEAR II station in Hyytiälä, Finland. Concentrations of 1-2 nm ions measured with the NAIS are also included. Sub-2 nm (2-2.3 nm) ion concentrations measured with CIC are refered as Channel 1 (Channel 2-Channel 3) in the .csv file.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Dataset for publication: "Magnesium and Aluminum in Contact with Liquid Battery Electrolytes: Ion Transport through Interphases and in the Bulk"
<div> </div> <div> <p>This is the experimental raw data set associated with the following publication: M. Löw, J. Grill, MM May, and J. Popovic-Neuber, Magnesium and Aluminium in Contact with Liquid Battery Electrolyte: Ion Transport through Interphases and in the Bulk, ACS Material Letters (2024). DOI:10.1021/acsmaterialslett.4c01589</p> <p>The data set is organized according to the publication's figures. </p> </div>
Trapped ion mobility spectrometry-guided molecular discrimination between plasmalogens and other ether lipids in lipidomics experiments
<p>Supplementary Dataset for the manuscript "Trapped ion mobility spectrometry-guided molecular discrimination between plasmalogens and other ether lipids in lipidomics experiments" under preparation for bioRxiv submission.<br>Data is packed into a zip archive according to ZENODO upload limitations.<br>in the top folder the actual analysis and respective files, as described in the publications supplementary materials can be found as <em>PLOP2 </em>folder (including raw data etc.).<br>The additional images explain the repo layout in a sketched form: <br>Additionally also for data readout the used <em>docker-compose.yml </em>and a github clone of the important <em>timsr </em>package is included.<br>PLEASE READ the README!.<br><br>Additionally the file `Calibration-2021-08-31_11-44-22.pdf` contains the calibration report, and `PLOP4_method.pdf` a hystar report of the used methodology.<br><br>For LSI Reporting Checklist see DOI: 10.5281/zenodo.13963972</p>
Data set: Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "Modeling of Ion Agglomeration in Magnesium Electrolytes and its Impacts on Battery Performance", published in ChemSusChem (<span>2020</span><span>, </span><span>13 (14)</span><span>, 3599-3604, </span>DOI: 10.1002/cssc.202001034).</p> <p><br>The choice of electrolyte has a crucial influence on the performance of rechargeable magnesium batteries. In multivalent electrolytes an agglomeration of ions to pairs or bigger clusters may affect the transport in the<br>electrolyte and the reaction at the electrodes. In this work the formation of clusters is included in a general model for magnesium batteries. In this model, the effect of cluster formation on transport, thermodynamics and kinetics is consistently taken into account. The model is used to analyze the effect of ion clustering in magnesium tetrakis(hexafluoroisopropyloxy)borate in dimethoxyethane as electrolyte. It becomes apparent that ion agglomeration is able to explain experimentally observed phenomena at high salt concentrations. </p>
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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.