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11 results for “surface engineering”
Uncovering the Triplet Ground State of Triangular Graphene Nanoflakes Engineered with Atomic Precision on a Metal Surface
<p>OPEN DATA related to the research publication:</p> <p>J. Li, S. Sanz, J. Castro-Esteban, M. Vilas-Varela, N. Friedrich, T. Frederiksen, D. Peña, and J. I. Pascual, <em>Uncovering the triplet ground state of triangular graphene nanoflakes engineered with atomic precision on a metal surface</em>, Phys. Rev. Lett. <strong>124</strong>, 177201 (2020) [arXiv:1912.08298]</p> <p>Abstract: Graphene can develop large magnetic moments in custom-crafted open-shell nanostructures such as triangulene, a triangular piece of graphene with zigzag edges. Current methods of engineering graphene nanosystems on surfaces succeeded in producing atomically precise open-shell structures, but demonstration of their net spin remains elusive to date. Here, we fabricate triangulenelike graphene systems and demonstrate that they possess a spin S=1 ground state. Scanning tunneling spectroscopy identifies the fingerprint of an underscreened S=1 Kondo state on these flakes at low temperatures, signaling the dominant ferromagnetic interactions between two spins. Combined with simulations based on the meanfield Hubbard model, we show that this S=1 π paramagnetism is robust and can be turned into an S=1/2 state by additional H atoms attached to the radical sites. Our results demonstrate that π paramagnetism of high-spin graphene flakes can survive on surfaces, opening the door to study the quantum behavior of interacting π spins in graphene systems.</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Observations of groundwater fluctuations and surface moisture content on a medium-grained, planar beach (Sand Engine, the Netherlands)
<p>These data are groundwater and beach surface moisture values collected during the MegaPex campaign between October 11 and 20, 2014 at the Sand Engine, The Hague, the Netherlands by MSc students and staff of the Coastal Research Group at Utrecht University, the Netherlands. The data were obtained at 8 locations in a cross-shore array on the intertidal and upper beach. During the measurements the beach was planar (1:30) and the median grain size was 0.365 mm. The data are supplemented with bed profiles along the instrument array. For further information and meta-data, please consult the readme.txt and the header of the individual text files in the zip-file.</p>
Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
Toxicity of Large and Small Surface-Engineered Upconverting Nanoparticles for In Vitro and In Vivo Bioapplications
Open the record for dataset details and reuse information.
GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
Malignant Pleural Disease Treated With Autologous T Cells Genetically Engineered to Target the Cancer-Cell Surface Antigen Mesothelin
ClinicalTrials.gov study NCT02414269. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Multi-parametric profiling of a large panel of engineered nanomaterials (ENM) points to surface chemistry as a key determinant of nanomaterial effects
GEO Series GSE148705. Homo sapiens. 96 samples. Type: Expression profiling by array.
Dataset for the study: "Engineering the Surface of Carbon Dots for Enhanced Photoluminescence and Controlled Plasmonic Interactions"
Open the record for dataset details and reuse information.
Design of Bone Tissue Engineering scaffolds using Triply Periodic Minimal Surfaces (TPMS-Gyroid) and the signed distance field (SDF)
<p><strong>Design of Bone Tissue Engineering scaffolds using Triply Periodic Minimal Surfaces (TPMS) and the signed distance field (SDF) :</strong></p><p>1.) <strong>PS200 :</strong> Pore Size 200 µm and Strut Size 200 µm</p><p>2.) <strong>PS350 :</strong> Pore Size 350 µm and Strut Size 200 µm</p><p>3.) <strong>PS550 :</strong> Pore Size 550 µm and Strut Size 200 µm</p><p>4.) <strong>PS750 :</strong> Pore Size 750 µm and Strut Size 200 µm</p><p>5.) <strong>PS1000 :</strong> Pore Size 1000 µm and Strut Size 200 µm</p><p> </p><p><strong>For Both Design and Finite Element Modeling Dataset is given below :</strong></p><p><strong>N Musthafa, Haja-Sherief</strong>. "Design and Finite Element Analysis of Triply Periodic Minimal Surfaces (gyroid) Scaffolds". Zenodo, August 23, 2023. <a href="https://doi.org/10.5281/zenodo.8276799">https://doi.org/10.5281/zenodo.8276799</a></p><p><strong>The related scientific article is given below:</strong></p><p><strong>N. Musthafa, Haja-Sherief </strong>et al. 2023. "<strong>In-Silico Prediction of Mechanical Behaviour of Uniform Gyroid Scaffolds Affected by Its Design Parameters for Bone Tissue Engineering Applications</strong>" Computation 11, no. 9: 181. <a href="https://doi.org/10.3390/computation11090181">https://doi.org/10.3390/computation11090181</a></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.