Project Details
Projekt
Hetero-aggregation of nanoparticles in advective environments
Applicant
Professor Dr. Andreas Kronenburg
Subject Area
Mechanical Process Engineering
Energy Process Engineering
Energy Process Engineering
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 462463789
Mixing and aggregation are key processes in the pharmaceutical and chemical industries, and process control can markedly influence the products’ characteristics. If multiple materials are used, the number of hetero-contacts, i.e. the number of contacts between particles of the different components, are crucial for the product’s properties. It is likely that this number can be controlled by the sequence of mixing, and the relative strength of mixing at the macroscale (that brings the components together) and at the microscale (that leads to collision and contact formation) will be decisive for the final product quality. While collision dynamics for small particles that are not influence by advective flow are well understood and can be modelled with decent accuracy, there is no validated approach for a statistical description of hetero-aggregation where particle sizes are far from being monodisperse and where initial inhomogeneities in particle distribution have a large effect on the final aggregate structure and quality. The proposed work intends to address issues related to aggregation of a two-component system with variable primary particle sizes ranging from about 5 to 200 nm. Langevin dynamics (LD) will be conducted to generate aggregates and their size distributions for a variety of process conditions. LD provide a suitable tool for such study as every single primary particle will be tracked and no assumptions will be made on the resulting aggregate structure. The simulations allow for a characterization of the hetero-aggregates including their quality (number of hetero-contacts) and for an analysis of collision frequencies as function of the aggregate characteristics and flow conditions. Conventional models for collision frequencies are not likely to hold, and deep learning methods for closure of the population balance equation is suggested instead.
DFG Programme
Priority Programmes
