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10 results for “Pixel Detector”
Datasets acquired using Dectris ARINA detector in paper "Using a fast hybrid pixel detector..." doi:10.1088/2515-7639/acf524
<p>Here are the raw data presented in the paper "Using a fast hybrid pixel detector for dose-efficient diffraction imaging beam-sensitive organic molecular thin films"<br> doi:10.1088/2515-7639/acf524</p> <p>It contains multiple datasets:</p> <p>SmB6: The sample is a monocrystalline domain of SmB6 oriented along the <110> zone axis, prepared with FIB by Elisabeth Mueller at PSI. Data was collected with a probe-corrected 200kV TEM microscope, by Daniel Stroppa (Dectris) supported by Mingjian Wu (FAU). Further experimental parameters are mentioned in the paper.</p> <p>Gd2O3 Ptycho: The sample is a quasi-2D poly-crystalline Gd2O3 supported on a carbon TEM grid provided by Baixu Zhu and Xingchen Ye (Indiana University Bloomington). Data was collected with a probe-corrected 200kV TEM microscope, by Philipp Pelz supported by Mingjian Wu. Raw data was a scan of 512x512; a crop of 256x256 was used for reconstruction as presented in the paper. Further experimental parameters are mentioned in the paper</p> <p>Gold thin film: poly-crystalline gold thin film (nominal thickness ~20 nm) deposited on SiN membrane, provided by Peter Denninger (FAU). Data was analyzed using ACOM in py4dstem (ver. 13.17). The analysis notebook is included in the zip. Further experimental parameters are mentioned in the paper.</p> <p>DRCNT_PCBM: bulk hetero-junction organic solar cell thin film provided by Christina Harreiss (FAU). Further experimental parameters (4D-SCED and NBD 4D-STEM) are mentioned in the paper. The same, but pre-processed version (in Gatan dm4 format) of the datasets are in the previous version of this publication.</p> <p>Data visualization and processing can be done with NOVENA software, freely available at DECTRIS website. Alternatively, the files can be opened using a HDF5 file reader.</p>
Nanobeam electron diffraction dataset from ion irradiated DIN 1.4970 austenitic stainless steel with G-phase precipitates collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam mode (NBED) from a sample of ion irradiated austenitic (FCC) stainless steel of the DIN 1.4970 specification, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p>The main features in this dataset are:</p> <ul> <li>a single crystal of the matrix is sampled, close to a 110 zone axis</li> <li>inside the matrix, irradiation induced G-phase precipitates of 10-20 nm in size can be found which contribute weakly to some of the diffraction patterns. From these patterns it is possible to derive the orientation relationship of the precipitates with respect to the matrix.</li> <li>irradiation also resulted in the formation of faulted frank loops, which also show up in some diffraction patterns.</li> </ul> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from DIN 1.4970 steel (composition by weight: 15% Ni, 15% Cr, 1.8% Mn, 1.2% Mo, 0.5% Ti, 0.5% Si, 0.1% C, Fe Bal.) with the intended application of nuclear fuel cladding material. The material was originally in the shape of thin walled tubes and cold worked to 24% (measured by cross sectional area reduction). The material was aged for 2 hours at 800 °C. It was then irradiated to 40 dpa surface damage as calculated using the SRIM program and the Kinchin and Pease model with displacement energy of 40 eV, using 4.5 MeV Fe<sup>2+</sup> ions with a flux of arround 9x10<sup>11</sup> ions.s<sup>-1</sup>.cm<sup>-2</sup>. The irradiation was performed at 600 °C. Full details on the material, irradiation conditions, and context can be found in:</p> <p>Cautaerts, N., Delville, R., Stergar, E., Pakarinen, J., Verwerft, M., Yang, Y., Hofer, C., Schnitzer, R., Lamm, S., Felfer, P., & Schryvers, D. (2020). The role of Ti and TiC nanoprecipitates in radiation resistant austenitic steel : A nanoscale study. <em>Acta Materialia</em>, <em>197</em>, 184–197. https://doi.org/10.1016/j.actamat.2020.07.022</p> <p>A TEM sample was prepared by regular focused ion beam (FIB) lift-out techniques in a Ga-ion FIB. Additional details on the dataset can be found in the paper and supplementary materials of</p> <p>Cautaerts, N., Rauch, E. F., Jeong, J., Dehm, G., & Liebscher, C. H. (2021). Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. <em>Scripta Materialia</em>, <em>201</em>, 113930. https://doi.org/10.1016/j.scriptamat.2021.113930</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>NBED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5). The probe diameter was ~ 1 nm with a semi-convergence angle of ~0.5 mrad. Data was collected on a TemCam-XF416 pixelated CMOS detector (TVIPS). The camera length as indicated in the operating software was 80 cm, and collected images were 1024 by 1024 in size (hardware binning of 4). The dataset comprises 260x200 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 50 GB in size and can be shared upon request to the author. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 256x256. A median filter was also applied to the data to remove pixel noise.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 260 x 200 pixels</p> <p>Image shape: 256 x 256 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01261 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation) and the pixel scale can be set through the axes manager. It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. The scan lines show "jitters" due to the unstable snake-scan pattern, hysteresis and instability.</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
<p>Scanning transmission electron microscopy data related to paper "Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation", <a href="https://doi.org/10.1017/S1431927620024307">https://doi.org/10.1017/S1431927620024307</a>.</p>
Integration of an event-driven Timepix3 hybrid pixel detector into a cryo-EM workflow
<p><strong>Abstract</strong></p> <p>The development of direct electron detectors has played a key role in low-dose electron microscopy imaging applications. Monolithic active-pixel sensor (MAPS) detectors are currently widely applied for cryogenic electron microscopy (cryo-EM); however, they have best performance at 300~kV, have relatively low read-out speed and only work in imaging mode. Hybrid pixel detectors (HPDs) can operate at any energy, have a higher DQE at lower voltage, have unprecedented high time resolution, and can operate in both imaging and diffraction modes. This could make them well-suited for novel low-dose life-science applications, such as cryo-ptychography, iDPC, and liquid cell imaging. Timepix3 is not frame-based, but truly event-based, and can record individual hits with 1.56~ns time resolution. Here, we present the integration of such a detector into a cryo-EM workflow and demonstrate that it can be used for automated data collection on biological specimens. The performance of the detector in terms of MTF and DQE has been investigated at 200~kV and we studied the effect of deterministic blur. We describe a single-particle analysis structure of \SI{3}{\angstrom} resolution and compare it with Falcon3 data collected under the same microscope. These studies could pave the way toward more efficient low-dose single-particle techniques.</p> <p><strong>Data description</strong></p> <p>Data has been split up in several different directories. In general: each directory contains individual READMEs</p> <p><strong>Flat fields</strong></p> <p>Collected on both TImepix3 and Falcon3 at 200 kV using a Tecnai Arctica microscope. These data have been used for calculating NPS, ToT correction and gain correction. </p> <p><strong>Knife edge</strong></p> <p>Collected on both TImepix3 and Falcon3 at 200 kV using a Tecnai Arctica microscope. These data have been used for calculating MTF.</p> <p><strong>ToT correction calibration file</strong></p> <p>This calibration file has been used to correct all raw Timepix3 data. Including micrographs deposited in EMPIAR.</p> <p><strong>Gain correction</strong></p> <p>Gain correction files calculated from flat field data for several different image formation methods of the Timepix3. The Python script for calculating the gain has been included.</p> <p><strong>Software</strong></p> <p>The software tpx3HitParser, tpx3EventViewer and the MTF-NPS-DQE scripts have listed as related identifiers to this entry.</p> <p> </p>
Precession electron diffraction dataset from nanocrystaline Cu-Ag (FCC) alloy collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam + precession mode (PED) of a nanocrystaline Cu-Ag sample, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from a nanocrystalline Cu-Ag thin film. The details are described in the following publication:</p> <p>Oellers, Tobias, et al. "Thin-Film Microtensile-Test Structures for High-Throughput Characterization of Mechanical Properties." <em>ACS combinatorial science</em> 22.3 (2020): 142-149.</p> <p>The sample was prepared by punching a 3 mm diameter disc out, gluing this to a Cu single hole grid, and subsequent Ar+ ion milling until perforation at 2.5 kV using a PIPS II system (Gatan). The rough milling was followed with a 0.5 kV cleaning.</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>PED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5) and a condenser aperture size of 10 μm. The probe diameter was ~ 1 nm with a convergence angle of ~2 mrad. A precession frequency of 100 Hz and a precession angle of 0.5° were applied during the nanobeam scanning. Data was collected on a TemCam-XF416 pixelated CMOS<br> detector (TVIPS). The camera length as indicated in the operating software was 15 cm, and collected images were 2k by 2k in size (hardware binning of 2). The dataset comprises 150x150 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 100 GB in size and is no longer available. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 512x512. A median filter was also applied to the data.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 150 x 150 pixels</p> <p>Image shape: 512 x 512 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01155 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation). It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. A large number of the scan points are worthless. In addition, the detector background was not properly subtracted in the image, resulting in striped artifacts in the images.</p>
Ultra-High Granularity Pixel Vertex Detector (PXD) signature Images
<p>Pixel Vertex Detector <strong>(PXD)</strong> is the innermost sub-detector of Belle II. The configuration of the PXD consists of 40 sensors within two detector layers. The inner layer has 16 sensors, and the outer layer comprises 24 sensors. Thus, each event includes 40 grey-scale images, each with a resolution of 250x768 pixels, resulting in more than <strong>7.5 million pixel channels per event</strong>. Each event in the dataset has the structure of <strong>[40,1,250,768]</strong> where the events are encoded by an event number distributed over 40 sub-directories corresponding to the 40 class labels.</p> <p>1.1.1/ --> sensor #1<br> ├── event_1<br> ├── event_2<br> ├── ...<br> 1.1.2/ --> sensor #2<br> ├── event_1<br> ├── event_2<br> ├── ...</p> <p>The samples are simulated synthetic background processes by GEANT4 via the <a href="https://link.springer.com/article/10.1007/s41781-018-0017-9">basf2</a> software. This data is used to train/test the <a href="https://arxiv.org/abs/2303.08046">IEA-GAN: Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning</a>.</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing and Storage
<p>Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing, and Storage": <a href="https://doi.org/10.1017/S1431927620001713">https://doi.org/10.1017/S1431927620001713</a></p>
Fresnel imaging of ferromagnetic domain wall motion using fast pixelated electron detector
<p>This deposit contains supplementary datasets and data processing scripts used in the Master Thesis by Rajith Aravinth at the Norwegian University of Science and Technology (NTNU).</p>
Using Powder Diffraction Patterns to Calibrate the Module Geometry of a Pixel Detector
<p>Powder diffraction data and jupyter notebooks to accompany the publication: </p> <p>"Using Powder Diffraction Patterns to Calibrate the Module Geometry of a Pixel Detector"</p> <p><a href="https://sciprofiles.com/profile/142400">Jonathan P. Wright</a>, <a href="https://sciprofiles.com/profile/author/Wjg3TE9sVlJleUs2bC9IaVdNeXczVFlwK2p6eW9JTWhoREJiSEZnaWw1TT0=">Carlotta Giacobbe</a> and <a href="https://sciprofiles.com/profile/author/V3JYR2UvT1pjTFdPMWF6Y0pidGdNcHlaQndxMHJOK3pNTkFaRmZaMkU5UT0=">Eleanor Lawrence Bright</a></p> <p><em>Crystals</em> <strong>2022</strong>, <em>12</em>(2), 255; <a href="https://doi.org/10.3390/cryst12020255">https://doi.org/10.3390/cryst12020255</a></p>
A 100-pixel photon-number-resolving detector unveiling photon statistics
<p>Dataset and Monte Carlo code for "A 100-pixel photon-number-resolving detector unveiling photon statistics" published in Nature Photonics.</p> <p>Article link: https://www.nature.com/articles/s41566-022-01119-3</p>
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