Study of the interplay among melt morphology, rheology and 3D printability of Poly(Lactic Acid)/Poly(3-Hydroxybutyrate- Co-3-Hydroxyvalerate) blends

Polymeric materials made from renewable sources that can biodegrade in the environment are attracting considerable attention as substitutes for petroleum-based polymers in many fields, including additive manufacturing and, in particular, Fused Deposition Modelling (FDM). Among the others, poly(hydroxyalkanoates) (PHAs) hold significant potential as candidates for FDM since they meet the sustainability and biodegradability standards mentioned above. However, the most utilised PHA, consisting of the poly(hydroxybutyrate) (PHB) homopolymer, has a high degree of crystallinity and low thermal stability near the melting point. As a result, its application in FDM has not yet attained mainstream adoption. Introducing a monomer with higher excluded volume, such as hydroxyvalerate, in the PHB primary structure, as in poly(hydroxybutyrate-co-valerate) (PHBV) copolymers, reduces the degree of crystallinity and the melting temperature, hence improving the PHA printability. Blending amorphous poly(lactic acid) (PLA) with PHBV enhances further PHA printability via FDM. In this work, we investigated the printability of two blends characterised by different PLA and PHBV weight ratios (25:75 and 50:50), revealing the close connection between blend microstructures, melt rheology and 3D printability. For instance, the relaxation time associated with die swelling upon extrusion determines the diameter of the extruded filament, while the viscoelastic properties the range of extrusion speed available. Through thoroughly screening printing parameters such as deposition speed, nozzle diameter, flow percentage and deposition platform temperature, we determined the optimal printing conditions for the two PLA/PHBV blends. It turned out that the blend with a 50:50 weight ratio could be printed faster and with higher accuracy. Such a conclusion was validated by replicating with remarkable fidelity high-complexity objects, such as a patient’s cancer-affected iliac crest model.