Particle Dynamics in
Heterogeneous
Systems

Population balance models (PBMs) enable the simulation of agglomeration and breakage processes. However, the complexity increases in multi-material systems due to heterogeneous surface properties and the need to consider all pairwise interactions.
In this project, a multi-scale approach is used to determine the required kinetic rates (kernels): CFD-DEM simulations at both macro- and micro-scale are integrated with experimental data. Machine learning models are then used to translate these rates for use in both discrete and Monte Carlo PBMs. The overall goal is to establish a systematic methodology for the calculation of hetero-agglomeration processes in arbitrary multi-material systems. This enhances process transparency and control for a variety of applications.

Nanoparticles are widely used in industrial products because they combine material inherent physio-chemical properties with high surface to volume ratios. These properties are multiplied and complemented by the functional mixing with other nanoparticle species. Subsequently, hetero-contacts are formed at the distinct interface of the two materials, which are of fundamental importance for the desired functional properties of the mixture.
This project focuses on the improvement of carbon black properties for battery applications by hetero-aggregation with silica. The characterization of the hetero-contact and the resulting mixing quality of the hetero-aggregates is still a major challenge. Hence, the development of an extensive image analysis of the mixing quality with TEM in combination with multi-scale particle analysis by SAXS and ADC is pursued. The combination of both methods provides holistic information on all relevant length scales, leading to an understanding of the interaction between mixing quality and aggregate properties. This comprehensive insight into the quantity and quality of the hetero-contacts is pivotal to bridge the gap from aggregate properties to functional properties and the resulting material functions. Ultimately, it is envisioned to determine which nanoscale structure is most beneficial for the application in batteries in order to improve the manufacturing process of batteries as well as the electro-chemical performance.

This research project aims to reduce production costs and increase the product quality of battery cells. The concept is based on an innovative and agile production technology that allows flexible battery formats with the possibility of a quick recipe change for continuous cell production. The aim is to achieve sustainable production with minimal use of raw materials, minimizing raw material waste and paste loss. The process control strategy is based on an adaptive, digital concept to ensure high paste quality with the highest degree of automation and minimum energy consumption. Continuous mixing using an extruder is a recommended option to avoid a process interruption with the subsequent process steps.

The aim of the AgiloBat cluster project is to develop an agile production system for manufacturing cell assemblies from format-flexible pouch cells with modular process control and end-to-end quality assurance. The fully automated production system consists of robot cells in which a controllable microenvironment is maintained. This eliminates the need for large drying rooms and avoids the introduction of moisture by humans. We are investigating and modelling the mixing and dispersion effect of a twin-screw extruder for process integration in battery cell production. The development of a digital twin with SPH and compartment models contributes to the simulation-based optimization of the process. By integrating inline measurement technology, the mixing process can be monitored in real time.

As our society continues to rely heavily on digital interactions, cryptography and blockchain technology play a critical role in shaping a trustworthy and transparent future. Digital one-way functions (d-OWF), exemplified by cryptographic hash functions like SHA-256 form the backbone of secure communications and decentralized ledgers. In essence, they quickly provide a deterministic output for any given input, while it is impossible to analytically find the corresponding input for any given output (inverse problem). The most prominent application example of is the cryptocurrency Bitcoin where consensus on which transactions are added to the blockchain is reached without a central institution by the so-called proof of work (PoW). It involves solving specific inverse problems of SHA-256 by trial and error and requires computational power (work) on a massive scale.

Due to the ever-increasing environmental impact of modern blockchain applications, the PDHS group aims to establish a new field of research focusing on so-called physicochemical one-way functions (pc-OWF). In a nutshell, processes in particle technology are being investigated that serve as a replacement for the deterministic and digital OWF. Encryption is based on unique process outputs (e.g. product specifications) and a vast input space regarding process parameters. Decryption, i.e. a solution of an inverse problem, requires time and effort, forming an applicable Proof of Physical Work for blockchain technology.