Research data for: "Analyzing the 3D morphology of particle systems coated by mechano-fusion using micro-CT image data"
Contributing person | Nora Brachhold | |
Contributing person | Tom Kirstein | |
Contributing person | Urs A. Peuker | |
Contributing person | Volker Schmidt | |
Contributing person | Christos G. Aneziris | |
Documentation of the data | Raw micro-CT data is provided as 16-bit grayscale TIF image stacks with a voxel size of 0.4099 µm. Three segmentation levels are included: phase-wise (8-bit), aggregate-wise (16-bit), and host-only (16-bit). Images can be processed with standard tools supporting TIF stacks (e.g., ImageJ/Fiji, Python, MATLAB). SEM images are 8-bit grayscale TIFs at 0.12 µm pixel size. The included ilastik model (Pixel-seg.ilp) enables segmentation of SEM images into three classes using the free ilastik software (https://www.ilastik.org/download). Full acquisition and reconstruction parameters are documented in the accompanying README file. | |
Description of the data | The dataset consists of one README.md file and two zip archives. The file micro-CT.zip contains micro-CT image data organized into two folders, Lower_FOV and Upper_FOV, each holding four subfolders: Raw_images (raw reconstructed image slices in TIF format), Segmentation_phasewise (phase-wise segmentation of host, guest, air inclusion and background), Segmentation_final_aggregatewise (aggregate-wise segmentation with unique gray values per aggregate), Segmentation_hosts (host-particle-only segmentation), and a preview image. The file high-res-SEM.zip contains high-resolution SEM images inside the folder images, further subdivided into five cross-section folders (Cross-section-1 to Cross-section-5), and a pre-trained ilastik pixel classification model (Pixel-seg.ilp) for segmentation of SEM images into alumina-host, PTFE-guest, and background classes. | |
Type of the data | Dataset | |
Total size of the dataset | 2584758691 | |
Author | Seyffer, Judith Miriam | |
Author | Gräfensteiner, Phillip | |
Author | Rieder, Philipp | |
Upload date | 2026-06-17T11:02:45Z | |
Publication date | 2026-06-17T11:02:45Z | |
Data of data creation | 2023-06 | |
Publication date | 2026-06-17 | |
Abstract of the dataset | This dataset contains micro-computed tomography (micro-CT) and high-resolution scanning electron microscopy (SEM) image data of alumina host particles coated with polytetrafluoroethylene (PTFE) guest particles via mechano-fusion, a dry particle coating process. The data supports quantitative analysis of 3D coating morphology using geometric descriptors including coating thickness, surface area coverage, and contact loss. Micro-CT scans of the in epoxy resin embedded particles were acquired with a Zeiss Xradia 510 Versa across two fields of view (upper and lower part) of a 1 mm × 1 mm × 2 mm sample. The dataset includes raw reconstructed image stacks alongside phase-wise, aggregate-wise, and host-only segmentation outputs generated via a hierarchical segmentation pipeline developed as part of the research. SEM images of five polished cross-sections of the same sample were acquired with a Tescan Amber using a backscattered electron detector. An ilastik pixel classification model for SEM image segmentation is also provided. This dataset is associated with the publication "Analyzing the 3D morphology of particle systems coated by mechano-fusion using micro-CT image data." | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/2473 | |
Public reference to this page | https://doi.org/10.25532/OPARA-1322 | |
Publisher | Technische Universität Bergakademie Freiberg | |
Licence | Attribution 4.0 International | en |
URI of the licence text | http://creativecommons.org/licenses/by/4.0/ | |
Specification of the discipline(s) | 4 | |
Specification of the discipline(s) | 4::42::403::403-03 | |
Specification of the discipline(s) | 4::43::405::405-03 | |
Title of the dataset | Research data for: "Analyzing the 3D morphology of particle systems coated by mechano-fusion using micro-CT image data" | |
Research instruments | X-ray microscope Zeiss Xradia 510 Versa (Carl Zeiss Microscopy GmbH, Oberkochen, Germany) | |
Research instruments | Tescan Amber (Tescan, Brno, Czech Republic) | |
Underlying research object | In epoxy-resion embedded coated particles after the mechano-fusion process | |
Underlying research object | Host particle: Denka Alumina DAW-45 | |
Underlying research object | Guest particle: PTFE FP30-PD-000110 Goodfellow | |
Software | Zeiss Reconstructor | |
Software | ORS Dragonfly | |
Software | Zeiss Scout-and-Scan | |
Project abstract | The project "Agglomeration applying high intensity mixing (mechano-fusion) – An integrated approach towards the synthesis of tailored hetero-aggregates combining experiments with 2D and 3D structural characterization via image analysis and stochastic modeling" within the priority program 2289 funded by the German Research Foundation (DFG) applies mechano-fusion, a high-intensity mixing and dry particle coating process, to produce hetero-agglomerates from different primary particles. The mechanism of agglomeration and coating is based on dynamic de-agglomeration and re-agglomeration processes, whereby the high shear and compression forces within the machine create stable particle-particle contacts. The process is capable of both applying a (nano) guest particle coating to a larger host particle and generating hetero-agglomerates from (nano-)particles themselves. The investigations consider the dispersive and material-specific influencing factors of the primary particles as well as influencing process parameters in order to generate defined agglomerates. The hetero-agglomerates are intensively characterized concerning their structure (e.g. particle size distribution via laser diffraction, BET specific surface area, ...) and their macroscopic behavior. A special focus is the application of 2D and 3D imaging techniques to determine e.g. the mixing state of the primary particles in the respective agglomerates. The analysis of image data sets also provides detailed insight into the 3D architecture of the agglomerates. In cooperation with project partners, statistical data mining allows determining the multivariate distribution of polydisperse structural parameter vectors. Realistic hetero-agglomerates are simulated using spatial stochastic modeling, which provides a sufficiently large database for the use of machine learning tools. | |
Funding Acknowledgement | The research was funded as part of the priority program 2289 "Hetero-aggregates" (grant number: 462365306) by the German Research Foundation (DFG). | |
Public project website(s) | https://www.uni-bremen.de/spp2289 | |
Public project website(s) | https://gepris.dfg.de/gepris/projekt/441399220 | |
Project title | SPP 2289 Hetero-aggregates |
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