The present work was carried out within the framework of the priority programme SPP 2045. Technical ultra–fine particle systems (< 10μm) from highly specific separation processes are to be investigated here with regard to multi–dimensional property distributions. Tomographic measurement methods allow a comprehensive 3D description of particle–discrete data sets of statistically relevant size. The focus of the work is on X–ray tomographic analysis by means of micro-computed tomography (micro–CT), which, if necessary, is extended to several size scales by including further measurement methods (nano–CT) and supplemented by suitable elemental analysis (FIB–SEM + EBSD, EDX). Two preparation methods (wax, epoxy resin) for different particle preparations are described methodically, which have already been published in a case study or are the subject of current studies in the outlook of the work. Finally, a networked multiple use of the generated data within an online particle database is shown and its application is explained using three concrete examples.:1 Outline
2 Description of Particle Properties
2.1 Integral or Class–Based Description
2.2 Particle–Discrete Description
2.2.1 2D Description
2.2.2 Full 3D Description
2.3 Multidimensional Characterization on Basis of Particle–Discrete 3D Data
2.3.1 Motivation
2.3.2 Kernel Density Approach
2.3.3 Copula Approach
3 X–ray Tomography
3.1 Historical Context
3.2 X–ray Physics
3.2.1 X–ray Generation
3.2.2 Polychromatic Spectrum
3.2.3 Interaction with Matter
3.3 Tomographic Imaging
3.3.1 Motivation
3.3.2 Basic Idea
3.3.3 X–ray Microscopy Measurement Setup andWorkflow
3.3.4 Tomographic Reconstruction via Filtered Back Projection
3.3.5 Region of Interest Tomography
3.4 Relevant Artefacts Related to Particle Measurement
3.4.1 Temperature Drift
3.4.2 Penumbral Blurring and Shadow
3.4.3 Cone Beam
3.4.4 Out–of–Field
3.4.5 Center Shift
3.4.6 Sample Drift
3.4.7 Beam Hardening
3.4.8 Rings
3.4.9 Noise
3.4.10 Partial Volume
3.4.11 Summary
4 Practical Implementation
4.1 Particle Sample Requirements
4.1.1 Geometry
4.1.2 Dispersity and Homogeneity
4.2 Statistics
4.2.1 Single Particle Properties
4.2.2 Properties of a Limited Number of Particles (10 to several 100)
4.2.3 Particle Populations with Distributed Properties
4.3 2D Validation
4.4 Measurement
4.4.1 X–ray Microscope
4.4.2 Source Filter
4.4.3 Detector Binning
4.4.4 Cone Beam Artefact Compensation
4.4.5 Center Shift Correction
4.4.6 Dynamic Ring Removal
5 Image Analysis
5.1 Image Quality
5.1.1 Grey Value Histogram
5.1.2 Resolution
5.1.3 Signal–to–Noise Ratio
5.1.4 Contrast and Dynamic Range
5.1.5 Sharpness
5.1.6 Summary
5.2 Basic Image Processing Strategies
5.2.1 Threshold–Based Segmentation
5.2.2 Machine Learning Assisted Segmentation
6 Correlative Tomography
6.1 Scouting Approach
6.2 Multiscale Approach
6.3 Multidisciplinary Approach
7 Data Management
7.1 Data Quality
7.2 Data Availability
7.2.1 Tomographic Datasets
7.2.2 Particle Database
8 Outlook on Further Research Activities
9 Publications
9.1 Copyright Declaration
9.2 Overview
9.3 List of Publications
Paper A, Preparation techniques for micron–sized particulate samples in X–ray microtomography
Paper B, Self–constructed automated syringe for preparation of micron–sized particulate samples in X–ray microtomography
Paper C, Preparation strategy for statistically significant micrometer–sized particle systems suitable for correlative 3D imaging workflows on the example of X–ray microtomography
Paper D, Multi–scale tomographic analysis for micron–sized particulate samples
Paper E, PARROT: A pilot study on the open access provision of particle discrete tomographic datasets
10 Appendix
10.1 Application Example 1: Fracture Analysis
10.2 Application Example 2: 3D Contact Angle Measurement
10.3 Influence of the Source Filter
10.4 Influence of the X–rays on the Sample
10.5 Appropriate Filter Settings
10.6 Log File Parser / Die vorliegende Arbeit ist im Rahmen des Schwerpunktprogramms SPP 2045 entstanden. Technische Feinstpartikelsysteme (< 10μm) aus hochspezifischen Trennprozessen sollen hier hinsichtlich mehrdimensionaler Eigenschaftsverteilungen untersucht werden. Tomographische Messverfahren erlauben dabei eine vollständige 3D Beschreibung partikeldiskreter Datensätze statistisch relevanter Größe. Der Schwerpunkt der Arbeit liegt auf der röntgentomographischen Analyse mittels Mikro–Computertomographie (mikro–CT), die im Bedarfsfall unter Einbeziehung weiterer Messmethoden (nano–CT) auf mehrere Größenskalen erweitert und durch geeignete Elementanalytik (FIB–SEM + EBSD, EDX) ergänzt wird. Methodisch werden zwei Präparationsverfahren (Wachs, Epoxidharz) für unterschiedliche Partikelpräparate beschrieben, welche in einer Fallstudie bereits veröffentlicht bzw. im Ausblick der Arbeit Gegenstand aktueller Studien ist. Schließlich wird eine vernetzte Mehrfachnutzung der erzeugten Daten innerhalb einer online-Partikeldatenbank gezeigt und deren Anwendung an drei konkreten Beispielen erläutert.:1 Outline
2 Description of Particle Properties
2.1 Integral or Class–Based Description
2.2 Particle–Discrete Description
2.2.1 2D Description
2.2.2 Full 3D Description
2.3 Multidimensional Characterization on Basis of Particle–Discrete 3D Data
2.3.1 Motivation
2.3.2 Kernel Density Approach
2.3.3 Copula Approach
3 X–ray Tomography
3.1 Historical Context
3.2 X–ray Physics
3.2.1 X–ray Generation
3.2.2 Polychromatic Spectrum
3.2.3 Interaction with Matter
3.3 Tomographic Imaging
3.3.1 Motivation
3.3.2 Basic Idea
3.3.3 X–ray Microscopy Measurement Setup andWorkflow
3.3.4 Tomographic Reconstruction via Filtered Back Projection
3.3.5 Region of Interest Tomography
3.4 Relevant Artefacts Related to Particle Measurement
3.4.1 Temperature Drift
3.4.2 Penumbral Blurring and Shadow
3.4.3 Cone Beam
3.4.4 Out–of–Field
3.4.5 Center Shift
3.4.6 Sample Drift
3.4.7 Beam Hardening
3.4.8 Rings
3.4.9 Noise
3.4.10 Partial Volume
3.4.11 Summary
4 Practical Implementation
4.1 Particle Sample Requirements
4.1.1 Geometry
4.1.2 Dispersity and Homogeneity
4.2 Statistics
4.2.1 Single Particle Properties
4.2.2 Properties of a Limited Number of Particles (10 to several 100)
4.2.3 Particle Populations with Distributed Properties
4.3 2D Validation
4.4 Measurement
4.4.1 X–ray Microscope
4.4.2 Source Filter
4.4.3 Detector Binning
4.4.4 Cone Beam Artefact Compensation
4.4.5 Center Shift Correction
4.4.6 Dynamic Ring Removal
5 Image Analysis
5.1 Image Quality
5.1.1 Grey Value Histogram
5.1.2 Resolution
5.1.3 Signal–to–Noise Ratio
5.1.4 Contrast and Dynamic Range
5.1.5 Sharpness
5.1.6 Summary
5.2 Basic Image Processing Strategies
5.2.1 Threshold–Based Segmentation
5.2.2 Machine Learning Assisted Segmentation
6 Correlative Tomography
6.1 Scouting Approach
6.2 Multiscale Approach
6.3 Multidisciplinary Approach
7 Data Management
7.1 Data Quality
7.2 Data Availability
7.2.1 Tomographic Datasets
7.2.2 Particle Database
8 Outlook on Further Research Activities
9 Publications
9.1 Copyright Declaration
9.2 Overview
9.3 List of Publications
Paper A, Preparation techniques for micron–sized particulate samples in X–ray microtomography
Paper B, Self–constructed automated syringe for preparation of micron–sized particulate samples in X–ray microtomography
Paper C, Preparation strategy for statistically significant micrometer–sized particle systems suitable for correlative 3D imaging workflows on the example of X–ray microtomography
Paper D, Multi–scale tomographic analysis for micron–sized particulate samples
Paper E, PARROT: A pilot study on the open access provision of particle discrete tomographic datasets
10 Appendix
10.1 Application Example 1: Fracture Analysis
10.2 Application Example 2: 3D Contact Angle Measurement
10.3 Influence of the Source Filter
10.4 Influence of the X–rays on the Sample
10.5 Appropriate Filter Settings
10.6 Log File Parser
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:80212 |
| Date | 12 September 2022 |
| Creators | Ditscherlein, Ralf |
| Contributors | Peuker, Urs A., Spiecker, Erdmann, TU Bergakademie Freiberg |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1016/j.powtec.2019.06.001, 10.1016/j.mex.2019.11.030, 10.1016/j.powtec.2021.09.038, 10.1017/s1431927620001737, 10.1017/S143192762101391X |
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