Systematic analysis of the extrusion process in 3D bioprinting is mandatory for process optimization concerning production speed, shape fidelity of the 3D construct and cell viability. In this study, we applied numerical and analytical modeling to describe the fluid flow inside the printing head based on a Herschel–Bulkley model. The presented analytical calculation method nicely reproduces the results of Computational Fluid Dynamics simulation concerning pressure drop over the printing head and maximal shear parameters at the outlet. An approach with dimensionless flow parameter enables the user to adapt rheological characteristics of a bioink, the printing pressure and needle diameter with regard to processing time, shear sensitivity of the integrated cells, shape fidelity and strand dimension. Bioinks consist of a blend of polymers and cells, which lead to a complex fluid behavior. In the present study, a bioink containing alginate, methylcellulose and agarose (AMA) was used as experimental model to compare the calculated with the experimental pressure gradient. With cultures of an immortalized human mesenchymal stem cell line and plant cells (basil) it was tested how cells influence the flow and how mechanical forces inside the printing needle affect cell viability. Influences on both sides increased with cell (aggregation) size as well as a less spherical shape. This study contributes to a systematic description of the extrusion-based bioprinting process and introduces a general strategy for process design, transferable to other bioinks.

In flame spray pyrolysis (FSP), the evolution of metal oxide nanoparticles relies on quite a number of droplet (liquid) and vapor phase related physical mechanism as for instance precursor evaporation, oxidation, nucleation via gas‐to‐particle conversion mechanism, and subsequent particle (solid) growth mechanisms based on coagulation, sintering/coalescence and agglomeration. The liquid precursor and dispersion oxygen feed rates are relevant control parameters of the FSP process for tailoring the nanoparticle size (diameter) and structure as well as the atomizer nozzle configuration. Sophisticated non‐intrusive, laser‐based in situ and ex situ diagnostics with multi‐scale spatial resolution (micrometer to meter range) are applied for analyzing droplet formation and size, gas velocity, temperature, species concentration, as well as primary and agglomerate diameters along the flow direction. Computational fluid dynamics (CFD) are coupled with population balance modeling (PBM) to elucidate the nanoparticle dynamics within the reactive spray. It is found that the CFD‐PBM approach allows estimations of primary and agglomerate nanoparticle diameters within 80% and 75% accuracy compared to experimental data, suggesting that the methods presented could pave the way for designing next‐generations flame reactors design.

Spray drying is used for the manufacture of many consumer and industrial products such as instant dairy and food products, laundry detergents, pharmaceuticals, ceramics, and agrochemicals. During spray drying, agglomerates of powder particles are formed which determine the instant properties of the powder. Agglomeration during spray drying is considered to be a difficult process to control. The main cause of this is the complex interaction of the process variables: the atomization process, the mixing of spray and hot air, the drying of suspension droplets and the collision of particles which might lead to coalescence or agglomeration. As a consequence, agglomeration during spray drying is operated by trial-and-error. In an EC–sponsored project, named the EDECAD projects, an industrially validated computer model, using CFD technology, to predict agglomeration processes in spray drying machines is developed. An Euler-Lagrange approach with appropriate elementary models for drying, collision, coales...