Supplementary Material for the Dissertation "Characterization of Coated Particles from Mechano-Fusion"
Contributing person | Gert Schmidt | |
Contributing person | Yvonne Volkmar | |
Contributing person | Annett Kästner | |
Contributing person | Allison Götz | |
Contributing person | Dikshita Nath | |
Contributing person | Debora Neuber | |
Contributing person | Nora Stefenelli | |
Contributing person | Harsh Vijay Salvi | |
Contributing person | Urs Alexander Peuker | |
Contributing person | Lisa Ditscherlein | |
Contributing person | Ralf Ditscherlein | |
Documentation of the data | The data comprise raw and processed measurement data, reconstructed and segmented image stacks, and derived particle properties. File formats include TIFF/TIF, CSV, TXT, XLS/XLSX, PNG, and PDF. CT data are provided as TIFF image stacks; segmented phases and optional thickness-filtered data are provided separately. AFM data include raw TIFF images, processed XYZ text files, force-distance data, and derived Excel results. Calculation and experimental metadata are provided in Excel workbooks. Detailed acquisition parameters, processing procedures, and software-specific information are provided in the README files contained in the respective archives. | |
References to related material | Disseration "Characterization of Coated Particles from Mechano-Fusion" by Judith Miriam Seyffer | |
Description of the data | This dataset is the Supplementary accompanying the cumulative dissertation "Characterization of Coated Particles from Mechano-Fusion". It contains the additional research data referred to in the dissertation (e.g., "see Supplementary"): pristine particle characterization, calculations, an example experimental protocol, atomic force microscopy (AFM) data as well as micro-computed tomography (micro-CT) and nano-CT image data of mechano-fusion (MF) coated particles. The dataset consists of six zip archives and a README file. All zip archives expect for one contain their own README file with with detailed information on devices, acquisition parameters and data processing. | |
Type of the data | Dataset | |
Total size of the dataset | 4128037259 | |
Author | Seyffer, Judith Miriam | |
Upload date | 2026-10-06T11:36:48Z | |
Publication date | 2026-10-06T11:36:48Z | |
Data of data creation | 2025 | |
Publication date | 2026-10-06 | |
Abstract of the dataset | The dry particle coating process of mechano-fusion (MF) is of interest for a variety of applications, ranging from pharmaceuticals to battery materials. In this work, MF was used to produce hetero-aggregate type coated particles. Dense, discrete and homogeneous monolayer coatings were formed for the particle system consisting of spherical alumina host particles and spherical polystyrene guest particles over a wide parameter space of the available MF device. Increasing the processing intensity, mainly by increasing the rotational speed, led to guest particle deformation and even comminution. The hetero-aggregate microstructure was investigated from different perspectives. Correlating a hetero-aggregate microstructural property (specific surface area) with the macroscopic property of flowability revealed distinct structure–property groups, detailing the influence of MF process parameters. Dry laser diffraction provided information beyond the apparent hetero-aggregate particle size, e.g., on the guest particle aggregation and deformation state. Insight into the 3D structure and morphology of the coating, as well as the guest particle connectivity, was gained through micro-computed tomography (micro-CT), by computing, e.g., coating thickness and guest particle distances. Atomic force microscopy allowed for the guest particle-discrete quantification of deformation. The analyses via micro-CT were extended to a model battery particle system, which exhibited continuous coatings. Since the observed hetero-aggregate structures are not specific to MF, the characterization workflows are transferable to other particle systems and dry coating processes. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/2912 | |
Public reference to this page | https://doi.org/10.25532/OPARA-1561 | |
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 | Supplementary Material for the Dissertation "Characterization of Coated Particles from Mechano-Fusion" | |
Research instruments | X-ray microscope: Zeiss Xradia 510 Versa (Carl Zeiss Microscopy GmbH, Oberkochen, Germany) | |
Research instruments | X-ray microscope: Zeiss Xradia 810 Ultra (Carl Zeiss Microscopy GmbH, Oberkochen, Germany) | |
Research instruments | Atomic Force Microscope: XE-100 (Park Systems, Gwacheon, Korea) | |
Research instruments | Gas adsorption: Micromeritics Gemini VII 5.03 (Norcross, Georgia, USA) | |
Research instruments | True density: Micromeritics AccuPyc II 1340, V2.01 (Norcross, Georgia, USA) | |
Research instruments | Laser diffraction: Malvern Panalytical Mastersizer 3000 (Kassel, Germany) | |
Research instruments | Laser diffraction: Sympatec HELOS (Clausthal-Zellerfeld, Germany) | |
Research instruments | Scanning electron microscope: FEI Europe XL 30 ESEM (Eindhoven, Netherlands) | |
Research instruments | Thermogravimetric analysis: NETZSCH STA 449 F3 (Selb, Germany) | |
Underlying research object | Host particle: Denka Alumina DAW-45 | |
Underlying research object | Guest particle: Soken Chemical Polystyrene SX350-H | |
Underlying research object | Powder (fine solid particles) formed of coated particles after the mechano-fusion process | |
Underlying research object | Host particle: Coating Products PMMA MH60-FD | |
Underlying research object | Host particle: Denka Alumina DAW-05 | |
Underlying research object | Guest particle: Goodfellow PTFE | |
Underlying research object | Guest particle: Soken Chemical PMMA MP-2801 | |
Underlying research object | Guest particle: Denka Alumina DAW-03 | |
Software | ImageJ 1.53t | |
Software | ilastik 1.4.0 | |
Software | Dragonfly 2025 | |
Software | Park Systems XEI 5.1.6 | |
Software | Gwyddion 2.63 | |
Software | Zeiss Reconstructor | |
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 | Dissertation Judith Seyffer, SPP2289 |
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