Computation of shape and structural parameters of particles from measurement data of combined measurement techniques

• Applicant: Professor Dr.-Ing. Michael Stintz
• Subject Area: Mechanical Process Engineering
• Term: from 2016 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 310875572

The research project is part of the German research cluster "Multi-parameter characterisation of particle-based functionalised materials by means of innovative online-measurement technique (MPaC)", which aims at the online characterisation of aerosol processes in industry. The objective is to derive different morphological parameters (i.e. on size, shape, structure) simultaneously for which purpose different measurement principles are to be combined. This approach requires adequate models that quantify the impact of particle morphology on the optical and aerodynamic properties of single particles and particle systems.Subject of this research project is the correlation between the morphological parameters of non-spherical particles and their optical and mobility-based properties. For the sake of feasibility only few types of particle morphology (structure classes) are considered: fractal-like or compact agglomerates of spherical particles, rod-like particles and agglomerates of them, and particles with defined surface structures (e. g. catalyst particles on inert grains). The size range reaches of a few nanometres to several micrometres (upper limit: agglomerate size and length of individual rods). Quantitative models for the calculation of optical and mobility-based properties can be partly elicited from literature; others have to be developed within the research project. These models are employed in order to establish sensor models, which are as accurate as necessary and as simple as possible. Reconstructing the real particle morphology will be the central approach for obtaining quantitative models with low computational costs. The intended sensor models can be expressed by means of mathematical functions or as data-base. The primary objective of all computations is the derivation of relevant morphological parameters (e. g. fractal dimension, agglomerate porosity, aspect ratio, surface roughness) out of data sets of combined measurements. Therefore, the information content of measurement data with respect to morphological parameters will be employed for the various measurement techniques and their combinations used with the research cluster. Such an information analysis reveals technique-specific limits of application and may even support the model-based selection of appropriate measurement techniques.

• DFG Programme: Research Grants
• Co-Investigator: Privatdozent Dr.-Ing. Frank Babick