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2,904 results for “Solute”
Solute dynamics in the hyporheic mesocosm in Watershed 1 at the H.J. Andrews Experimental Forest, 2019-2020
We investigated biogeochemistry along a 12-m hyporheic mesocosm that allowed for controlled testing of seasonal and spatial water quality changes along a flowpath with fixed geometry and constant flow rate. Water quality profiles of oxygen, carbon, and nitrogen were measured at 1-m intervals along the mesocosm over multiple seasons. dissolved oxygen (DO) and temperature profiles were monitored on 18 dates between May 2019 through August 2020. Grab samples to monitor profiles of carbon, nitrogen, and various other solutes along the mesocosm were collected in December 2019 and August 2020 to provide more comprehensive biogeochemical analyses at time points when the dissolved oxygen (DO) and temperature profiles were at or near the maximum seasonal differences. Mesocosm monitoring ceased abruptly due to the Holiday Farm Fire, which burned from September through October 2020, cutting off personnel access and electrical power to the mesocosm facility.
Solute dynamics in the hyporheic zone of a headwater stream in Watershed 1 at the Andrews Experimental Forest, 2016-2018
This project examined the interactions between stream water and subsurface sediment to quantify how these interactions influenced organic C respiration and dissolved inorganic C (DIC) production in the hyporheic zone of a high-gradient headwater mountain stream draining a forested catchment at the H. J. Andrews Experimental Forest, Oregon, USA. The study used six 2-m long hyporheic mesocosms which were packed with streambed sediment in the spring of 2016. The mesocosms are located at the Watershed 1 (WS1) stream gage and stream water from WS1 has been pumped through the mesocosms continuously since they were first packed through the end of (and beyond) this study in autumn of 2018. The mesocosms were designed around 1-m long 20-cm diameter aluminum pipe segments with sample ports located each meter along the flowpath through each mesocosm – thus sampling at the inlet, at 1 m, and at the outlet which represents the full 2-m long flow path. Sampling was conducted on seven dates between Oct 23 2016 and Aug 27 2018. On two of these dates, only background samples were collected. On the remaining 5 dates, sampling was designed around continuous-injection tracer experiments using both a conservative tracer (salt) and a reactive tracer (various dissolved organic substrates). For background sampling events, samples were generally only collected once. The tracer experiments involved 4 discreet sampling times: 1. pre-injection (under background conditions); 2. early plateau; 3. late plateau, and 4. post-injection (and in one injection experiment, a 5th sample at late-post-injection time). For each round of samples, the mesocosm water temperature, pH, EC, and DO were measured with sensors in a small flow-through cell. Then water samples were collected for laboratory analysis for both DOC and DIC. The median travel time of water through each pipe segment of the 2-m mesocosms was also calculated from the conservative tracer break-through curves.
S38 | SOLNSLMCTPS | SOLUTIONS Predicted Transformation Products by LMC
<p>This is the collection associated with list S38 SOLNSLMCTPS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S38 | SOLNSLMCTPS | <strong>SOLUTIONS Predicted Transformation Products by LMC</strong></p> <p>Predicted Transformation Products calculated by LMC during the SOLUTIONS project, interactive table available <a href="https://www.normandata.eu/solutions/modelsTransformationProducts.php">here</a>.</p> <p>14/11/19 update: added CSV version. 9/7/2025: fixed several corrupt SMILES and added InChIKeys to XLSX/CSV. Note that the author had to be changed to the University to satisfy Zenodo upload requirements, the original authors were listed as <a href="https://oasis-lmc.org/about/contacts.aspx">LMC</a>. </p>
Dataset of "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation"
Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). This is the first PES study of this amino acid in its most biologically relevant environment. Proline's structure in the aqueous phase under neutral conditions is zwitterionic, distinctly different from the non-ionic neutral form in the gas phase. By analyzing the carbon 1s and nitrogen 1s core-levels as well as the valence spectra of aqueous-phase proline, we found that the electronic structure is dominated by the protonation state of each constituent molecular site (the carboxyl and amine) with small yet noticeable interference across the molecule. The site-specific nature of the core-level spectra enables probing of individual molecular constituents. The valence photoelectron spectra are more difficult to interpret because of overlapping signals of proline with the solvent and pH-adjusting agents (HCl and NaOH). Yet we are able to reveal subtle effects of specific (hydrogen-bonding) interaction with the solvent on the electronic structure. We also demonstrate that the relevant conformational space is much smaller for aqueous-phase proline than it is for its gas phase analogue. This study suggests that caution must be taken when comparing photoelectron spectra for gaseous and aqueous-phase molecules, particularly if those molecules are readily protonated / deprotonated in solution.
Dataset of "Molecular Dynamics Simulations Unveil the Aggregation Patterns and Salting out of Polyarginines at Zwitterionic POPC Bilayers in Solutions of Various Ionic Strengths"
<p>Molecular dynamics simulations are performed for a series of model cell-penetrating peptides (in particular nona-arginines) in aqueous solutions, in contact with model phosphocholine (POPC) membranes in conditions of different ionic strengths. The unusual aggregation properties of peptides at model lipid bilayers are analyzed and different sizes and lifetimes of aggregates are presented.<br>This dataset contains molecular dynamics simulation data with trajectories, input files, and topology files for all studied systems. They contain low peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration.<br>In addition to low peptide concentration, high peptide concentration in water, low NaCl concentration, high NaCl concentration, low CaCl2 concentration, and high CaCl2 concentration are also studied.</p>
DAS Control over the spatial correlation of silica perforations in thin films as a function of solution conditions
<p><span>Dataset production context : A perforated silica layer with structural correlation is engineered using sol-gel chemistry, applied to large-scale flat and curved sur-faces. The anion(s) used in the preparation give tailored spatial correlation, and control over perforation size and density. Surface structuration is rapidly and reproducibly created using water and salts as inexpensive and ecofriendly reagents.</span></p>
S33 | SOLUTIONSMLOS | Chemicals used for Modelling in SOLUTIONS
<p>This is the collection associated with list S33 SOLUTIONSMLOS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S33 | SOLUTIONSMLOS | <strong>Chemicals used for Modelling in SOLUTIONS</strong></p> <p>SOLUTIONSMLOS contains the 6462 chemicals used for modelling in the SOLUTIONS project (<a href="http://www.solutions-project.eu/">www.solutions-project.eu/</a>), provided by Jaroslav Slobodnik (EI).</p> <p>Update 14 Nov 2019: added CSV file. 6 Feb. 2020 CSV with corrected SMILES entries for PubChem import. 6 Nov 2020 structure fix for CAS 111360-16-8 (reported by Leon, PubChem). 17 July 2022: more SMILES fixes, plus one InChIKey change. 18 June 2023: one more SMILES fix (YLMOTKLYENPQLK-VMPITWQZSA-N) in CSV only.</p>
How to measure work functions from aqueous solutions - data
<p>Data set pertaining to the article "How to measure work functions from aqueous solutions", <a href="https://doi.org/10.1039/D3SC01740K" target="_blank" rel="noopener">https://doi.org/10.1039/D3SC01740K</a> (Chemical Science <strong>14</strong>, 9574-9588 (2023)). A new protocol for energy referencing of photoemission data from liquids (<a href="https://doi.org/10.1039/D1SC01908B" target="_blank" rel="noopener">https://doi.org/10.1039/D1SC01908B</a>, Chemical Science <strong>12</strong>, 10558-10582 (2021)) is refined towards determining work functions from liquids.<br><br></p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus v2020.10 standard using the NXmpes user contributed format suggested by the Fairmat consortium, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are included:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br>2. As-measured data ('raw').</p> <p>Files with extension .txt are tab-separated ascii-files.</p> <p><br>The following files are provided:</p> <p>Photoemission data pertaining to solute measurements and reference measurements using a gold wire:<br>'Figure 3.h5'<br>'Figure 4.h5'<br>'Figure S1.h5'<br>'Figure S2.h5'<br>Kinetic energies are presented as measured. The scale offset of our spectrometer, determined as E_kin(corrected) = E_kin(measured) + 0.224 eV for data sets 'Figure 3.h5', 'Figure 4.h5' ,'Figure S2.h5', has not been taken into account.</p> <p>Numeric representations of the analysis results shown in the article's figures in graphical form:<br>'Figure 5.txt'<br>'Figure 6B.txt'<br>'Figure 7.txt'<br>'Figure S4B.txt'<br>'Figure S5.txt'</p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Chemistry of freely-draining soil solutions at the Hubbard Brook Experimental Forest, Watershed 1, 1996 - present
Tension-free lysimeters were installed in three soil horizons at 13 locations within Hubbard Brook Experimental Forest Watershed 1, representing the variation in elevation and forest type of the watershed. At each location lysimeters were placed beneath the Oa horizon and within the upper and lower B horizon. Soil solution samples were collected approximately monthly and analyzed for pH, acid neutralizing capacity (ANC), base cations, soluble anions, trace metals, nitrogen and carbon. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Watershed 3 – One year of resin-extracted solutes from variably saturated soils
Hbr363: WS3 One year of resin-extracted solutes from variably saturated soils The Lateral Weathering Study looks at spatial patterns of mineral weathering processes at Hubbard Brook Experimental Forest. This project is characterizing mineral and elemental depletion/enrichment, soil morphology and chemistry, solute transport, and groundwater chemistry along hydropedological gradients. This dataset provides the total elemental mass of inorganic solutes (Ca, Na, Mg, Al, Fe, Mn, P, and S) as well as dissolved organic carbon (DOC) that were extracted off resins installed into shallow groundwater wells (~30-100cm) in Watershed 3. Resin packs were deployed for a total of one year (August 2019-2020) with four consecutive deployment periods, to avoid overloading resin ion capacity. Total mass for each solute was accounted for an entire resin pack, which was 5cm in height and 5cm in diameter, containing approximately 90 g of resin. Resin packs were installed in three different topographic positions along three transects (sites = 9), to characterize solute mass fluxes through different hydropedological units.
Hubbard Brook Experimental Forest: Chemistry of freely-draining soil solutions in Watershed 6, 1984 - ongoing
Tension-free lysimeters were installed in three soil horizons at three locations adjacent to Hubbard Brook Experiment Forest Watershed 6. At each location lysimeters were placed beneath the Oa horizon and within the upper and lower B horizon. Soil solution samples were collected approximately monthly and analyzed for pH, acid neutralizing capacity (ANC), base cations, soluble anions, trace metals, nitrogen and carbon. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Seismic moment tensor solutions of Mw > 3.4 earthquakes occurred between 2002 and 2023 in the Southeastern Alps
<p>Seismic moment tensor solutions of 63 earthquakes with 3.4≤ Mw≤ 5.1 occurring from 2002 to 2023 in the Southeastern Alps and strict surroundings (latitude 45°N-47.5°N and longitude 10°E-15°E). The seismograms have been recorded and acquired by the OGS - North-Eastern Italy Seismic and Deformation Network (<a href="https://doi.org/10.7914/SN/OX">https://doi.org/10.7914/SN/OX</a>). </p> <p>For more details:</p> <p>Saraò A., Sugan M., Bressan G., Renner G., and Restivo A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021.</p> <p> </p>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.</p> <p>Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.<br><br></p> <p><strong>The dataset includes: </strong></p> <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C<br><br>For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.<br><br></li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period <br><br>For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul> <p> </p> <p>Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
A setup for studies of photoelectron circular dichroism from chiral molecules in aqueous solution - data
<p>Data set pertaining to the article "A setup for studies of photoelectron circular dichroism from chiral molecules in aqueous solution" | Review of Scientific Instruments, aip.org, doi: <a href="https://doi.org/10.1063/5.0072346">10.1063/5.0072346</a> .</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.06, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html</p> <p>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>Files with extension .asc are ascii-files.</p> <p><br> The following files are provided:<br> fig-fenchone-rsi.asc : numeric form of traces shown in Fig. 7<br> fig-lfenchone-roi-rsi.asc : numeric form of traces shown in Fig. 8</p> <p>data relevant for Fig.s 7,8 and Table 1<br> gas-phase_1R-fenchone.h5 : data set of gas phase photoemission data for (1R,4S)-(−)-fenchone<br> gas-phase_1S-fenchone.h5 : data set of gas phase photoemission data for (1S,4R)-(+)-fenchone<br> liquid-phase_1R-fenchone.h5 : data set of liqiud phase photoemission data for (1R,4S)-(−)-fenchone<br> liquid-phase_1S-fenchone.h5 : data set of liquid phase photoemission data for (1S,4R)-(+)-fenchone</p> <p>data relevant for Fig. 9<br> gas-liq_1R-fenchone.h5 : data set for photoemission of (1R,4S)-(−)-fenchone (biased and grounded)</p> <p>data relevant for Fig. 10<br> flatjet_fig10a.h5<br> flatjet_fig10b.h5<br> flatjet_fig10c.h5</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Growth of hexagonal boron nitride from molten nickel solutions: a reactive molecular dynamics study
<p>Authors: Amin Ahmadisharaf and Jeffrey Comer</p> <p><br>Publication: Amin Ahmadisharaf, Bin Liu, James H. Edgar, and Jeffrey Comer (2025) Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study. ACS Applied Materials & Interfaces. <a href="https://doi.org/10.1021/acsami.4c16991">doi.org/10.1021/acsami.4c16991</a></p> <p>Funding: Department of Energy Office of Science, grant DE-SC0021264, <a>https://pamspublic.science.energy.gov/WebPAMSExternal/Interface/Common/ViewPublicAbstract.aspx?rv=5e6ffff5-0daa-47e8-a3d0-20f594b7bfb8&rtc=24&PRoleId=10</a></p> <p>**************************************</p> <p>This data set for the manuscript entitled "Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software LAMMPS, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text. They include force field parameter files (in ReaxFF format), LAMMPS configuration files (*.in), ReaxFF control files (*.control), LAMMPS log files (*.log), and LAMMPS output including restart files (in binary LAMMPS format) and trajectories in dcd format (downsampled to 12.5 or 25 ps per frame) and also PDB and PSF files are useful for visualization with VMD. Analysis is performed by python and shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>The species analysis is performed by the VMD Tcl script "Figure3/analysis/count_hBN_species_NNB_BN.tcl" using the parameters given in "Figure3/analysis/doCount.sh".</p> <p>The directory contents are as follows.</p> <p>--------------------------------------------------------------<br>Figure-1: Parallel tempering simulations of the boron-nickel system and calculation of the boron concentration along the z-dimension of the nickel slab.</p> <p>The analysis of the boron concentration profile is performed by the VMD Tcl scripts calcRatioZRef.tcl and calcConcZRef.tcl using the parameters given in "Figure1/analysis/step3_conc_profile.sh". Also, the reorganization of the parallel tempering trajectories into frames at a single temperature is performed by the VMD Tcl script extractReplicaFrames.tcl based on "Figure1/analysis/step1_sort_frames.sh".</p> <p><br>--------------------------------------------------------------<br>Figure-2: Simulation of hBN sheet growth at 1750 K and calculation of largest cluster.</p> <p><br>--------------------------------------------------------------<br>Figure-3: Simulations of different boron-to-nickel ratios at varying nitrogen pressures at 1750 K and the calculation of the largest hBN cluster formed under different scenarios. The suffixes "liu", "long_liu", and "low_liu" correspond to pressures of 100.0, 50.0, and 25.0 atm respectively.</p> <p>The analysis of hBN clusters is performed by the VMD Tcl script "Figure3/analysis/" using the parameters given in "Figure3/analysis/doCount.sh". The related simulations files and outputs for panel A in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-4: Recognition and counting the different boron-nitrogen motifs in the simulation was performed in Figure 2.</p> <p>The related simulations files and outputs for panel B and C in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-5: Simulation of the temperature effect on hBN growth, and recognition and counting of the different boron-nitrogen motifs at 1750, 1800, 1900, 2000, 2200, and 2700 K.. </p> <p><br>--------------------------------------------------------------<br>Figure-6: Recognition of existing motifs for nitrogen atoms in the growth path of hBN and calculation of the probabilities of transitions between different motifs across all nitrogen atoms.</p> <p>The related simulations files and outputs for all panels in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-7: Comparing the ReaxFF and ab initio simulations of small B-N motifs(B--N--B and B--N) in a nickel slab and calculation of bond lengths and angle values.</p> <p><br>--------------------------------------------------------------<br>Figure-8: Diffusion simulations of four different systems at 1800 K: nickel with a single B atom, nickel with a single N atom, nickel with a free B-N-B molecule, and nickel with a small hBN sheet and Mean Squared Displacement (MSD) values were calculated and compared to assess the surface mobility of the different particles.</p> <p> </p>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis - Underlying CT data
<p>Underlying CT data of "<strong>Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis</strong>"<br><strong>DOI: <a href="https://doi.org/10.1208/s12249-024-02908-5">https://doi.org/10.1208/s12249-024-02908-5</a></strong></p> <p>by </p> <p>Konstantina Chachlioutaki, Anastasia Iordanopoulou, Orestis L. Katsamenis, Anestis Tsitsos, Savvas Koltsakidis, Pinelopi Anastasiadou, Dimitrios Andreadis, Vangelis Economou, Christos Ritzoulis, Dimitrios Tzetzis, Nikolaos Bouropoulos, Iakovos Xenikakis & Dimitrios Fatouros </p> <p> </p> <div> <h3>Authors and Affiliations</h3> <ol> <li> <p>Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece</p> <p>Konstantina Chachlioutaki, Anastasia Iordanopoulou, Iakovos Xenikakis & Dimitrios Fatouros</p> </li> <li> <p>Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece</p> <p>Konstantina Chachlioutaki & Dimitrios Fatouros</p> </li> <li> <p>μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</p> <p>Orestis L. Katsamenis</p> </li> <li> <p>Institute for Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK</p> <p>Orestis L. Katsamenis</p> </li> <li> <p>Laboratory of Animal Food Products Hygiene - Veterinary Public Health, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece</p> <p>Anestis Tsitsos & Vangelis Economou</p> </li> <li> <p>Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 14km Thessaloniki–N. Moudania, 57001, Thermi, Greece</p> <p>Savvas Koltsakidis & Dimitrios Tzetzis</p> </li> <li> <p>Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece</p> <p>Pinelopi Anastasiadou & Dimitrios Andreadis</p> </li> <li> <p>Department of Food Science and Technology, International Hellenic University, Sindos Campus, 57400, Thessaloniki, Greece</p> <p>Christos Ritzoulis</p> </li> <li> <p>Department of Materials Science, University of Patras, Rio, 26504, Patras, Greece</p> <p>Nikolaos Bouropoulos</p> </li> <li> <p>Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, 26504, Patras, Greece</p> <p>Nikolaos Bouropoulos</p> </li> </ol> </div>
Dataset of the paper Zeitler et al. (2021) : Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions
<p>The dataset consists of two .h5 files. "Dataset_DOGSOC_GROOPS.h5" contains the 12-hour thermospheric density scale factors of the satellites Starlette, Stella, and Larets of Chapter 4.2. Each path includes a file with three columns. The first column contains the time vector in JD2000.0. The second column and third column contain the scale factor time series (unfiltered, smoothed with a 10-day moving average filter). The following scale factor time series are available:</p> <ul> <li>DOGSOC/starlette</li> <li>DOGSOC/stella</li> <li>DOGSOC/larets</li> <li>GROOPS/starlette</li> <li>GROOPS/stella</li> <li>GROOPS/larets</li> </ul> <p> </p> <p>"Dataset_SLR_ACC.h5" contains the 12-hour thermospheric density scale factors from SLR measurements (DOGS-OC) to the satellites Starlette, WESTPAC, Stella, and Larets and from accelerometer measurements of the satellites GRACE and CHAMP of Chapter 4.1. Each path includes a file with three columns. Again, the first column contains the time vector in JD2000.0, and columns 2 and 3 contain the thermospheric density scale factors (unfiltered, smoothed with a 10-day moving average fitler). The following scale factor time series are available:</p> <ul> <li>ACC/CHAMP</li> <li>ACC/GRACE</li> <li>SLR/starlette</li> <li>SLR/westpac</li> <li>SLR/stella</li> <li>SLR/larets</li> </ul> <p>Further information about the data can be found in the file "description_of_datasets_v1.txt" or in the paper Zeitler et al. (2021): Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions. Journal of Geophysical Research: Space Physics.</p>
ScienceDex guides
Understand access before you commit
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.