Aerodynamic fractionation of airborne particles by means of classifying aerodynamic lens

Fractionating airborne particles based on multiple characteristics is becoming increasingly important in various industrial and research fields, such as mining and recycling. Recent advancements focus on the simultaneous characterization and fractionation of particles by multiple properties. This dissertation explores the fractionation of a technical aerosol containing a mix of micron-sized copper and silicon particles by size and material composition using a Classifying Aerodynamic Lens (CAL) setup. In this research project, the particle size distribution and material composition were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) for samples collected from the feed stream (upstream of CAL) and product stream (downstream of CAL) under different operational pressures. The experimental results generally align with predictions from an analytical fractionation model but also highlight the significance of particle shape as a third fractionation criterion.Additionally, this dissertation details the design process of the CAL. Multiple predictive models were employed to describe the transfer of particles through the CAL and to predict the material and size composition of the product stream based on both online and offline measurements of the feed stream. A model was also utilized to predict the signal of a Differential Aerodynamic Particle Sizer (DAPS), a measurement device based on a CAL. The findings presented in this work indicate that material-based fractionation is effective at low operational pressures even when particle species have similar aerodynamic properties. This discovery has significant implications for industries where precise particle fractionation is crucial.