Florian Hofmann (Würzburg, DE), Jessica Faber (Erlangen, DE), Silvia Budday (Erlangen, DE), Prof. Dr. Tomasz Jüngst (Würzburg, DE)
Abstract text (incl. figure legends and references)
Most human tissues contain cellular, structural, mechanical or biochemical gradients that influence the function of tissues and should be considered in biofabrication. A possible approach to generate gradients are microfluidic printheads, but their use in extrusion-based bioprinting is limited by the viscoelastic properties of bioinks and adjusted mixers are needed.
Microfluidic printheads are developed to generate defined mixing ratios for bioinks. The viability of cells after extrusion and the quality of the deposited structures are analysed.
The printheads were made using resin-based additive manufacturing. To evaluate the print quality, the mixers were cut in the middle, imaged and compared to the CAD data (figure 1). Simulations were used to estimate the mixing potential and compared to the deposition of patterns of dyed solutions with variable viscosities. Images were taken and evaluated with FIJI. Live-dead staining were used to analyse cell damage through mixing.
Constructs with five different mixing structures could be printed with channel diameters as small as 300 µm. The comparison of the CAD data and the microfluidic chips revealed an increasing shrinkage of 5% to 25% with decreasing channel diameter. Straight channels had a higher permeability and a lower mixing ratio than curved sections, with decreasing channel diameter. The simulations showed, that the obstacle geometry has the best mixing rate. The cell viability of the self-designed mixers is improved compared to commercial printheads.
This study shows that DLP printing is suitable to generate perfusable mixing units. Their performance to mix inks of different viscosities depends on the used geometry. Future experiments will implement mounting the printheads to an extrusion-based 3D bioprinter to generate gradients including cells.
Figure 1. Evaluation of a printhead with an obstacle mixing geometry. a) CAD data, b) Cut and coated chip, c) SEM image, d) Deposited structure.