Extrusion-based bioprinting is one of the most widespread technologies
due to its affordability, wide range of processable materials, and
ease of use. However, the formulation of new inks for this technique
is based on time-consuming trial-and-error processes to establish
the optimal ink composition and printing parameters. Here, a dynamic
printability window was modeled for the assessment of the printability
of polysaccharide blend inks of alginate and hyaluronic acid with
the intent to build a versatile predictive tool to speed up the testing
procedures. The model considers both the rheological properties of
the blends (viscosity, shear thinning behavior, and viscoelasticity)
and their printability (in terms of extrudability and the ability
of forming a well-defined filament and detailed geometries). By imposing
some conditions on the model equations, it was possible to define
empirical bands in which the printability is ensured. The predictive
capability of the built model was successfully verified on an untested
blend of alginate and hyaluronic acid chosen to simultaneously optimize
the printability index and minimize the size of the deposited filament.
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