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

该研究项目是德国研究集群“通过创新在线测量技术(MPAC)对基于颗粒的功能化材料进行多参数表征”的一部分，旨在对工业中的气溶胶过程进行在线表征。这种方法需要足够的模型来量化粒子形态对单个粒子和粒子系统的光学和空气动力学性质的影响。本研究的主题是非球形粒子的形态参数与其基于光学和迁移率的性质之间的相关性。为了可行，只考虑了几种类型的颗粒形态(结构类)：球形颗粒的分形状或致密凝聚体，棒状颗粒和它们的团聚体，以及具有特定表面结构的颗粒(如惰性颗粒上的催化剂颗粒)。计算光学性质和基于迁移率的性质的定量模型部分可以从文献中获得；其他模型必须在研究项目中开发。重建真实的粒子形态将是以较低的计算成本获得定量模型的主要方法。所需的传感器模型可以通过数学函数或数据库来表示。所有计算的主要目标是从组合测量的数据集中推导出相关的形态参数(例如，分维、团聚体孔隙率、纵横比、表面粗糙度)。因此，关于形态参数的测量数据的信息量将被用于与研究集群一起使用的各种测量技术及其组合。

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.