Multiscale Tomographic Analysis for Micron-Sized Particulate Samples.

multidimensional particle characterization multiscale X-ray tomography parametric copula statistical image analysis

Journal

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
ISSN: 1435-8115
Titre abrégé: Microsc Microanal
Pays: England
ID NLM: 9712707

Informations de publication

Date de publication:
Aug 2020
Historique:
pubmed: 7 7 2020
medline: 7 7 2020
entrez: 7 7 2020
Statut: ppublish

Résumé

The three-dimensional characterization of distributed particle properties in the micro- and nanometer range is essential to describe and understand highly specific separation processes in terms of selectivity and yield. Both performance measures play a decisive role in the development and improvement of modern functional materials. In this study, we mixed spherical glass particles (0.4–5.8 μm diameter) with glass fibers (diameter 10 μm, length 18–660 μm) to investigate a borderline case of maximum difference in the aspect ratio and a significant difference in the characteristic length to characterize the system over several size scales. We immobilized the particles within a wax matrix and created sample volumes suitable for computed tomographic (CT) measurements at two different magnification scales (X-ray micro- and nano-CT). Fiber diameter and length could be described well on the basis of the low-resolution micro-CT measurements on the entire sample volume. In contrast, the spherical particle system could only be described with sufficient accuracy by combining micro-CT with high-resolution nano-CT measurements on subvolumes of reduced sample size. We modeled the joint (bivariate) distribution of fiber length and diameter with a parametric copula as a basic example, which is equally suitable for more complex distributions of irregularly shaped particles. This enables us to capture the multidimensional correlation structure of particle systems with statistically representative quantities.

Identifiants

pubmed: 32627723
doi: 10.1017/S1431927620001737
pii: S1431927620001737
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

676-688

Auteurs

Ralf Ditscherlein (R)

Institute of Mechanical Process Engineering and Mineral Processing, Technische Universität Bergakademie Freiberg, D-09599Freiberg, Germany.

Orkun Furat (O)

Institute of Stochastics, Ulm University, D-89069Ulm, Germany.

Mathieu de Langlard (M)

Institute of Stochastics, Ulm University, D-89069Ulm, Germany.

Juliana Martins de Souza E Silva (J)

Institute of Physics - mikroMD, Martin Luther University Halle-Wittenberg, D-06120Halle, Germany.

Johanna Sygusch (J)

Helmholtz Institute Freiberg for Resource Technology, D-09599Freiberg, Germany.

Martin Rudolph (M)

Helmholtz Institute Freiberg for Resource Technology, D-09599Freiberg, Germany.

Thomas Leißner (T)

Institute of Mechanical Process Engineering and Mineral Processing, Technische Universität Bergakademie Freiberg, D-09599Freiberg, Germany.

Volker Schmidt (V)

Institute of Stochastics, Ulm University, D-89069Ulm, Germany.

Urs A Peuker (UA)

Institute of Mechanical Process Engineering and Mineral Processing, Technische Universität Bergakademie Freiberg, D-09599Freiberg, Germany.

Classifications MeSH