Cell seeding of 3D scaffolds is a critical step in tissue engineering since the final tissue properties are related to the initial cell distribution and density within the scaffold unit. Perfusion systems can transport cells to the scaffold however; the fact that cells flow inside the scaffold pores does not guarantee cell deposition onto the scaffold substrate and cell attachment. The aim of this study was to investigate how fluid flow conditions modulate cell motion and deposition during perfusion. For such a purpose, a multiphase-based computational fluid dynamics (CFD) model was developed in conjunction with particle tracking velocimetry experiments (PTV) which for the first time were applied to observe cell seeding inside a 3D scaffold. CFD and PTV results showed the strong effect of gravity for lower flow rates leading to cell sedimentation and poor transport of cells to the scaffold. Higher flow rates help overcome the effect of gravity so more cells travelling inside the scaffold were found. Nonetheless, fluid flow drags cells along the fluid streamlines without intercepting the scaffold substrate. As a consequence, if cells do not deposit into the scaffold substrate, cell adhesion cannot occur. Therefore, cell-scaffold interception should be promoted and the present computational model which predicts the effect of gravity and fluid drag on cells trajectories could serve to optimise bioreactors and enhance cell seeding efficiency.

Transport properties of 3D scaffolds under fluid flow are critical for tissue development. Computational fluid dynamics (CFD) models can resolve 3D flows and nutrient concentrations in bioreactors at the scaffold-pore scale with high resolution. However, CFD models can be formulated based on assumptions and simplifications. μ-Particle image velocimetry (PIV) measurements should be performed to improve the reliability and predictive power of such models. Nevertheless, measuring fluid flow velocities within 3D scaffolds is challenging. The aim of this study was to develop a μPIV approach to allow the extraction of velocity fields from a 3D additive manufacturing scaffold using a conventional 2D μPIV system. The μ-computed tomography scaffold geometry was included in a CFD model where perfusion conditions were simulated. Good agreement was found between velocity profiles from measurements and computational results. Maximum velocities were found at the centre of the pore using both techniques with a difference of 12% which was expected according to the accuracy of the μPIV system. However, significant differences in terms of velocity magnitude were found near scaffold substrate due to scaffold brightness which affected the μPIV measurements. As a result, the limitations of the μPIV system only permits a partial validation of the CFD model. Nevertheless, the combination of both techniques allowed a detailed description of velocity maps within a 3D scaffold which is crucial to determine the optimal cell and nutrient transport properties.