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opencc-by-4.0Nov 2020View details →

Raw data for article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" Gostin et al 2018

This repository contains raw data for the article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" by Gostin et al. 2018 in Advanced Healthcare Materials, 7, 1800338 (https://doi.org/10.1002/adhm.201800338). Most data comes from one beamtime at the Diamond synchrotron in the UK in May 2016.  It consists of X-ray diffraction images taken in situ in artificial corrosion pits on a Ti-based metallic glass. Please see the README file for more details.

opencc-by-4.0Nov 2020View details →

X-ray diffraction images of bovine trypsin crystals recorded at the FemtoMAX beamline of Max IV synchrotron facility

The deposition concerns bovine trypsin diffraction images in two wedges. Each image is recorded on a still crystal and separated by 0.1 deg rotation. The x4.tar.gz archive contains summed intensities from individual snapshots at the same orientation, whereas x4_single.tar.gz archive contains single snapshots/orientation.

opencc-by-4.0Nov 2023View details →

Synchrotron X-ray Computed Tomography scan of a wasp

<h4>Contents:</h4><ul><li>bee_yazeed-20231001T170032.h5 - SXCT scan of a wasp performed at beamline <a href="https://www.sesame.org.jo/beamlines/beats">ID10-BEATS</a> of SESAME.</li><li>SESAME_wasp_yazeed.avi - 3D video rendering of phase-contrast CT reconstruction of bee_yazeed-20231001T170032. The dataset was reconstructed using <a href="https://github.com/gianthk/alrecon/tree/master">alrecon</a>. The video was created using ORS Dragonfly.</li></ul><h4>H5 dataset information:</h4><ul><li>Raw experimental data (sinogram, flat fields and dark fields) and metadata are stored in a common .H5 file.</li><li>The HDF5 file is organized hierarchically following the <a href="https://dxfile.readthedocs.io/en/latest/">Scientific Data Exchange (DXfile)</a> community standard.</li></ul><h4>How to reconstruct:</h4><ul><li>You can use <a href="http://www.silx.org/">Silx</a> to read and explore the .H5 dataset.</li><li>The file can be read within Python using the <a href="https://dxchange.readthedocs.io/en/latest/">DXChange</a> package.</li><li>See the <a href="https://beats.readthedocs.io/reconstruction.html">ID10-BEATS beamline user guide</a> for a detailed description on how to process and reconstruct the scan.</li></ul>

opencc-by-4.0May 2018View details →

Armoricaphyton chateaupannense - Propagation Phase Contrast X-Ray Synchrotron Microtomography Dataset

Includes: 1) Propagation phase contrast X-ray synchrotron microtomography (PPC-SRμCT) dataset to study the three-dimensional structure of the permineralized wood from Armoricaphyton chateaupannense, using the ID19 beamline of the European Synchrotron Radiation Facility (ESRF), Grenoble, France. Dataset Information (also see scan_log.xml): <ul> <li>Number of image in dataset: 2159 images</li> <li>Images prefix: plante_</li> <li>Image x/y size: 3763 x 2048 px</li> <li>Image type: 16-bit TIFFs (with Pack Bits compression)</li> <li>Image size on disk: 14.8 MB each</li> <li>Scan date: 31-Oct-2008</li> <li>Scan energy: 30keV </li> <li>Voxels size: 0.551 um</li> <li>Filters: Al_1_mm Al_0.5_mm Diam_U</li> <li>Projection number: 4000</li> <li>Projection rotation: 360 degs</li> <li>Magnification: x20</li> <li>Source-sample distance: 145000</li> <li>Scan type: continuous</li> </ul> Note: these images have been cropped from their original scan output size. 2) Two supplemental videos of the 3D model.

opencc-by-4.0Jun 2024View details →

Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography

Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.

opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Analysis - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from six differently orientated Ti-6Al-4V (Ti-64) samples. Two different refinement methods were used to fit a range of diffraction pattern ring intensities, for determining crystallographic texture in both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases. The first procedure was based on an established Rietveld refinement method, using the software package <a href="https://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a>. The second procedure uses a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. Both methods were used to calculate texture from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples. Material The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. Six different rectangular samples were cut from this material and are referenced according to alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector; <table align="center"> <caption>A table recording the SXRD run number and sample orientation analysed.</caption> <thead> <tr> <th scope="col">Run Number</th> <th scope="col">Sample Orientation Reference</th> <th scope="col"> Sample Orientation (Horizontal - Vertical) </th> </tr> </thead> <tbody> <tr> <td>103840</td> <td>Sample 6</td> <td>TD45ºRD - ND</td> </tr> <tr> <td>103841</td> <td>Sample 5</td> <td>RD - TD45ºND</td> </tr> <tr> <td>103842</td> <td>Sample 4</td> <td>TD - RD45ºND</td> </tr> <tr> <td>103843</td> <td>Sample 3</td> <td>RD - TD</td> </tr> <tr> <td>103844</td> <td>Sample 2</td> <td>RD - ND</td> </tr> <tr> <td>103845</td> <td>Sample 1</td> <td>TD - ND</td> </tr> </tbody> </table> Diffraction Pattern Averaging The .cbf images found in the <a href="https://doi.org/10.5281/zenodo.7311306">raw dataset</a> were first converted into .tiff images. The stage-scan images were then averaged together for each of the different sample orientations, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>, to produce six averaged .tiff images. These averaged .tiff image capture average diffraction peak intensities from an area of about 96.75 mm2 (equivalent to a total volume of around 193.5 mm3) from each piece, which is therefore representative of bulk crystallographic texture from six different sample orientations. MAUD Analysis To process data using MAUD the diffraction pattern images must first be caked, which converts the data into .dat files of intensity versus 2θ profiles, using 72 azimuthal cakes, each of 5° azimuthal width. Although MAUD has an in-built function to cake data, using ImageJ, it is not possible to cake data in MAUD with ImageJ in an automated way. Therefore, caking was done using <a href="https://pyfai.readthedocs.io/en/master/">pyFAI</a>, an open-source Python package, with the caking procedure recorded in a separate Python notebook, <a href="https://github.com/LightForm-group/pyFAI-integration-caking">pyFAI-integration-caking</a>. The caking was applied to each of the six averaged tiff images, as well as being applied to 387 individual X-Y stage-scan tiff images from Sample 1 (103845). The caking procedure was also applied to the CeO2 calibrant diffraction pattern, creating a .dat file that could be used for calibration of the instrument parameters within MAUD, before fitting the experimental data from the different samples. A separate package <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> was used to record the setup of the files and details of the refinement procedure. Details about the refinement procedure are also recorded in an accompanying paper reporting on these results. A number of refinement steps were used to fit the caked data from the six different sample orientations, and calculate texture. Texture was also calculated from a .dat file that combined all six sample orientations together. The crystallographic texture was refined using the E-WIMV algorithm, which was found to best reproduce quantitative texture intensity values with an orientation distribution function (ODF) resolution of 15º. The MAUD-batch-analysis package also contains details about how to setup and run MAUD in an automated batch processing mode. MAUD's batch mode was used to calculate texture from a series of 387 individual stage-scan diffraction patterns from Sample 1 (103845). A MAUD-batch-analysis script was first used to substitute caked data from the 387 diffraction patterns into template .par files, which contained an initial refinement of the volume fraction, crystal sizes and micro-strain, as a starting point. Both the crystal parameters and texture were then iteratively refined, in MAUD, using a .ins batch analysis script launched from the terminal. This was done to refine both α and then β phase texture. The texture data from the MAUD analysis was recorded as an ODF, with 15º resolution over all Euler space, and extracted in text format using a script from MAUD-batch-analysis. These text files can be loaded into <a href="https://mtex-toolbox.github.io">MTEX</a>, for plotting and analysing both the α and β phase crystallographic texture. Continuous-Peak-Fit Analysis A .poni calibration file was created using <a href="https://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a CeO2 standard diffraction pattern image. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 21 α and 4 β lattice plane rings from the Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can then be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 21 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the six different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. This method was also used to analyse all 387 individual diffraction patterns recorded across Sample 1 (S1 – 103845), to quantify the texture variation across the piece. Metadata An accompanying YAML text file contains associated processing metadata for both the MAUD and the Continuous-Peak-Fit analyses, recording information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Dataset - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of raw synchrotron X-ray diffraction (SXRD) images, recording crystallographic texture from two different pre-processed Ti-6Al-4V (Ti-64) materials, analysing six differently orientated samples from each material. The aim of the work was to provide a large dataset for testing and improving crystallographic texture refinement from SXRD patterns, with the use of different computational fitting methods. Prior to the experiment, the Ti-64 materials had been pre-rolled and then air-cooled to develop the microstructure, rolling to 50% and 87.5% reduction at 915ºC using a rolling mill at The University of Manchester. Rectangular samples (2 mm thick) were then machined from these rolled blocks. The samples were cut along different directions, three samples along different orthogonal rolling directions, and three at different angles to the rolling directions. The samples are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens.  Data was recorded using a high energy 99.8 keV synchrotron X-ray beam and a 5 second exposure at the detector. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phase peaks. The SXRD data was recorded across each of the specimens by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments, forming a rectangular grid of measurement points across each sample. A powder Ti-64 sample was also measured as a random texture standard. As well as the main experiment, 3 samples (sample 1, 2 and 3) were held together in different orders (1, 2, 3 ; 2, 1, 3 ; 2, 3, 1) and analysed through-thickness, to measure how beam attenuation might affect the bulk texture measurement. In addition, different detector exposure times (1 to 0.04 seconds) were also tested to analyse the impact of exposure time on overall intensity, to see how well the α and β peaks could be resolved from background noise at very fast acquisition frequencies. The raw data is in the form of synchrotron diffraction pattern images which has been separated according to experiment type. An accompanying YAML text file contains associated beamline metadata for each measurement. Further details of the experimental setup can be found in a pdf document. The material data folder contains further details about the material and sample orientations, including an electron backscatter diffraction (EBSD) map that can be used to verify the crystallographic texture.

opencc-by-4.0Dec 2022View details →

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions. Material Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet. SXRD Data Collection Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample. Diffraction Pattern Averaging The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm2 (equivalent to a total volume of ~ 60 mm3), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition. SXRD Data Analysis A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations. A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 22 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the three different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. Metadata An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors. The material data folder documents the machining of the samples and the sample orientations. The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

opencc-by-4.0Dec 2022View details →

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)

A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a> Material The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below. <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> Sample Number </td> <td> Rolling Condition </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> MTEX Data Analysis The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values. Metadata An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.

opencc-by-4.0Dec 2021View details →

Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic

Text-fig. 7. Scanning electron micrographs (a, b, d, e, g–k), X-ray microtomographic orthoslices (c) and synchrotron radiation X-ray tomographic microscopy orthoslices (f) of fruits and endocarps of uncertain affinity from Zliv-Řídká Blana locality. a–c: Trebecenia sarcocalis, a – tricarpellate fruit, no. NM-F 3637, b – fruits supported by pentamerous and persistent calyx, no. NMF 3637, c – fruit almost circular in transverse section, no. NM-F 3637; d: Taxon 17, small fruit with slightly sunken stylar region, no. NM-F 3201; e: Taxon 19, spherical fruit, the fruit wall composed of large isodiametric, thick walled cells, no. NM-F 3181; f: Taxon 19, single-seeded fruit, no. NM-F 3621; g: Taxon 20, syncarpous, multicarpellate fruit of ten carpels, no. NM-F 3200; h: Taxon 22, syncarpous, multicarpellate fruit of seven carpels, no. NM-F 3159; i: cf. Sabia menispermoides, endocarp of drupaceous fruits, no. NM-F 4624; j: Taxon 25, endocarp triangular in cross-section, no. NM-F 3218; k: Taxon 24, endocarp spherical in cross-section with a distinctly ribbed and foveolate surface, no. NM-F 3217.

FIGURE 2 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 2. Reconstructed tomographic images of the specimen (1) and its internal structure in median section (2). The lower and upper jaws are enlarged in (3) and (4), respectively.

FIGURE 5 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 5. Three-dimensional reconstruction of the upper and lower jaws preserved in the body chamber of the specimen. The reconstructed parts are inside the specimen (1). The jaws are preserved close to each other (2).

FIGURE 1 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 1. Left lateral (1), dorsal (2) and ventral (3) views of Phyllopachyceras ezoensis with preserved upper and lower jaws in situ within the body chamber. UMUT MM 27831 (modified from Tanabe et al., 2013).

FIGURE 7 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 7. Result of segmentation of the upper jaw of the specimen, from frontal (1), rear (2), left-lateral (3) views and the transverse section of the area (4) indicated as a square in (3). The three-dimensional reconstruction (5) shows areal distributions of the "chitinous" lamellae and the calcareous covering. The reconstruction of the transverse section (6), which corresponds to (4), shows the architecture of the outer lamella. The abbreviations are indicated in (5).

FIGURE 6 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 6. Result of segmentation of the lower jaw of the specimen, from lateral view which is restricted to its anterior and posterior portion (1). Three-dimensional reconstruction (2) suggests a wide distribution of calcareous material. The outer calcareous layer on the outer "chitinous" layer is partly taken off in (2). The transverse section of the area indicated as a square in (1) shows that the calcareous covering of the lower jaw also covers the internal surface of the "chitinous" lamella (3). The abbreviation is indicated in (2).

FIGURE 4 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 4. Linear absorption coefficient (LAC) of the internal portions of the specimen estimated by their mean luminance values in the tomographic images. The numbers (1)-(10) correspond to the materials in Table 1. The dashed lines indicate the known values for the materials (Chantler et al., 2005) that could be expected to be observed in the specimen. Note that glycine is the most dominant amino acid in jaws of Octopus vulgaris (Hunt and Nixon, 1981). The relationship between LAC values and luminance values is based on the assumption that the LAC values for the surrounding air are zero and that the crystals precipitated in the phragmocone are calcite.

FIGURE 3 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography

FIGURE 3. Serial cross-sections of the body chamber portion of the specimen cut from the venter (1) to the dorsum (4), in which sectioned images of the upper jaw are shown. Note that the vertical stripes are due to the separated scanning.

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Text-fig. 3. Scanning electron micrographs (a, b) and synchrotron radiation X-ray tomographic microscopy orthoslices (c–e) of flower of Lambertiflora elegans gen. et sp. nov. from the Early Cretaceous Puddledock locality, Virginia, USA (holotype, PP53796, Puddledock sample 082). a) Flower in lateral view showing long pedicel and overlapping elongated tepals; b) Detail of flower showing overlapping elongated tepals; note the numerous holes indicating the position of probable secretory cells; c) Flower in longitudinal section showing overlapping elongated tepals, remains of probable poorly developed stamens or staminodes and probable poorly developed carpels on the central conical gynoecial region of the receptacle (cut between orthoslices xz0510 and 0570); d) Flower in longitudinal section (comparable to c) showing overlapping tepals, poorly developed stamens or staminodes, and probable poorly developed carpels on the central conical gynoecial region of the receptacle; note the prominent cavities from secretory cells scattered through the tissues (cut between orthoslice xz0560 and 0575); e) Flower in transverse section showing overlapping tepals, poorly developed stamens or staminodes, and remains of probable poorly developed carpels (cut between orthoslices xy1160 and 1180). Scale bars = 1 mm (a), 500 µm (b–e). in Multiparted, Apocarpous Flowers From The Early Cretaceous Of Eastern North America And Portugal

Text-fig. 3. Scanning electron micrographs (a, b) and synchrotron radiation X-ray tomographic microscopy orthoslices (c–e) of flower of Lambertiflora elegans gen. et sp. nov. from the Early Cretaceous Puddledock locality, Virginia, USA (holotype, PP53796, Puddledock sample 082). a) Flower in lateral view showing long pedicel and overlapping elongated tepals; b) Detail of flower showing overlapping elongated tepals; note the numerous holes indicating the position of probable secretory cells; c) Flower in longitudinal section showing overlapping elongated tepals, remains of probable poorly developed stamens or staminodes and probable poorly developed carpels on the central conical gynoecial region of the receptacle (cut between orthoslices xz0510 and 0570); d) Flower in longitudinal section (comparable to c) showing overlapping tepals, poorly developed stamens or staminodes, and probable poorly developed carpels on the central conical gynoecial region of the receptacle; note the prominent cavities from secretory cells scattered through the tissues (cut between orthoslice xz0560 and 0575); e) Flower in transverse section showing overlapping tepals, poorly developed stamens or staminodes, and remains of probable poorly developed carpels (cut between orthoslices xy1160 and 1180). Scale bars = 1 mm (a), 500 µm (b–e).

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Text-fig. 2. Synchrotron radiation X-ray tomographic microscopy volume renderings (a, b) and orthoslices (c–e) of Mugideiriflora portugallica gen. et sp. nov. from the Early Cretaceous Catefica locality, Portugal (holotype, S174254, Catefica sample 150). Yellow dots – stamens, red dots – carpels. a) Flower in lateral view showing the broad bases of the laminar tepals; b) Flower in longitudinal section showing the flat to slightly concave floral receptacle with a central conical gynoecial region (cut between orthoslices yz0800 and 1220); c) Flower in transverse section showing the numerous laminar tepals in several series and the stamens cut in the region of the poorly differentiated anthers; note cellular differences between outer (op) and inner (in) perianth parts, as well as and transverse sections of anthers, apparently with laterally to slightly dorsally placed pollen sacs (arrow heads) (cut at orthoslice xy0770); d) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0820); e) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0920); f) Flower in longitudinal section showing the shallowly concave floral receptacle with laminar tepals, stamens, and a central conical gynoecial region bearing poorly differentiated carpels (cut at orthoslice yz0900); g) Flower in longitudinal section perpendicular to that in (f) showing stamens and poorly differentiated carpels (cut at orthoslice xz1630). Scale bars = 1 mm (a, b), 500 µm (c–g). in Multiparted, Apocarpous Flowers From The Early Cretaceous Of Eastern North America And Portugal

Text-fig. 2. Synchrotron radiation X-ray tomographic microscopy volume renderings (a, b) and orthoslices (c–e) of Mugideiriflora portugallica gen. et sp. nov. from the Early Cretaceous Catefica locality, Portugal (holotype, S174254, Catefica sample 150). Yellow dots – stamens, red dots – carpels. a) Flower in lateral view showing the broad bases of the laminar tepals; b) Flower in longitudinal section showing the flat to slightly concave floral receptacle with a central conical gynoecial region (cut between orthoslices yz0800 and 1220); c) Flower in transverse section showing the numerous laminar tepals in several series and the stamens cut in the region of the poorly differentiated anthers; note cellular differences between outer (op) and inner (in) perianth parts, as well as and transverse sections of anthers, apparently with laterally to slightly dorsally placed pollen sacs (arrow heads) (cut at orthoslice xy0770); d) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0820); e) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0920); f) Flower in longitudinal section showing the shallowly concave floral receptacle with laminar tepals, stamens, and a central conical gynoecial region bearing poorly differentiated carpels (cut at orthoslice yz0900); g) Flower in longitudinal section perpendicular to that in (f) showing stamens and poorly differentiated carpels (cut at orthoslice xz1630). Scale bars = 1 mm (a, b), 500 µm (c–g).