Interest in electrospun polymeric nano-microfibers for tissue engineering applications has rapidly grown during the last decade. In spite of this technique’s flexibility and ability to form complex fiber assemblies, additional studies are required to elucidate how the fibrous microstructure translates into specific tissue (or meso-scale) level mechanical behavior. Deterministic structural models can quantify how key structures contribute to the mechanical response as a function of bulk deformation across multiple scales, as well as provide a better understanding of cellular mechanical response to local micro-structural deformations. Our ultimate aim is to utilize such models to assist tissue engineering scaffold design. In the current work, we present a novel approach to automatically quantify key micro-architectural descriptors (fiber overlaps, connectivity, orientation, and diameter) from SEM images of electrospun poly (ester urethane) urea (PEUU) to recreate statistically equivalent scaffold mechanical models. An appropriate representative volume element (RVE) size was determined by quantifying the point of stabilization of the architectural descriptors over image areas of increasing size. Material models were then generated specifying: fiber overlap density, fiber orientation, connectivity and fiber diameter. Macro-meso mechanical response was predicted via FEM simulations.

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