Abstract

In the present study, polydimethylsiloxane (PDMS) porous scaffolds are designed based on minimal surface architectures and fabricated through a low-cost and accessible sacrificial mold printing approach using a fused deposition modeling (FDM) 3D printer. The effects of pore characteristics on compressive properties and fluid permeability are studied. The results suggest that radially gradient pore distribution (as a potential way to enhance mechanically-efficient scaffolds with enhanced cell/scaffold integration) has higher elastic modulus and fluid permeability compared to their uniform porosity counterparts. Also, the scaffolds are fairly strain-reversible under repeated loading of up to 40% strain. Among different triply periodic minimal surface pore architectures, P-surface was observed to be stiffer, less permeable and have lower densification strain compared to the D-surface and G-surface-based pore shapes. The biocompatibility of the created scaffolds is assessed by filling the PDMS scaffolds using mouse embryonic fibroblasts with cell-laden gelatin methacryloyl which was cross-linked in situ by UV light. Cell viability is found to be over 90% after 4 days in 3D culture. This method allows for effectively fabricating biocompatible porous organ-shaped scaffolds with detailed pore features which can potentially tailor tissue regenerative applications. Statement of SignificancePrinting polymers with chemical curing mechanism required for materials such as PDMS is challenging and impossible to create high-resolution uniformly cured structures due to hard control on the base polymer and curing process. An interconnected porous mold with ordered internal architecture with complex geometries were 3D printed using low-cost and accessible FDM technology. The mold acted as a 3D sacrificial material to form internally architected flexible PDMS scaffolds for tissue engineering applications. The scaffolds are mechanically stable under high strain cyclic loads and provide enough pore and space for viably integrating cells within the gradient architecture in a controllable manner.

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