The emergence of COVID-19 has spurred demand for facemasks and prompted many studies aiming to develop masks that provide maximum protection. Filtration capacity and fit define the level of protection a mask can provide, and the fit is in large part determined by face shape and size. Due to differences in face dimensions and shapes, a mask of one size will not be likely to fit all faces. In this work, we examined shape memory polymers (SMPs) for producing facemasks that are able to alter their shape and size to fit every face. Polymer blends with and without additives or compatibilizers were melt-extruded, and their morphology, melting and crystallization behavior, mechanical properties, and shape memory (SM) behavior were characterized. All the blends had phase-separated morphology. The mechanical properties of the SMPs were modified by altering the content of polymers and compatibilizers or additives in the blends. The reversible and fixing phases are determined by the melting transitions. SM behavior is caused by physical interaction at the interface between the two phases in the blend and the crystallization of the reversible phase. The optimal SM blend and printing material for the mask was determined to be a polylactic acid (PLA)/polycaprolactone (PCL) blend with 30% PCL. A 3D-printed respirator mask was manufactured and fitted to several faces after being thermally activated at 65°C. The mask had excellent SM and could be molded and remolded to fit a variety of facial shapes and sizes. The mask also exhibited self-healing and healed from surface scratches.
Keywords: 3D printing; PCL; PLA; SMP; facemask; polymer extrusion; respirator; shape memory polymer.