Influence of the properties of different graphene-based nanomaterials dispersed in polycaprolactone membranes on astrocytic differentiation
Marián Mantecón-Oria, Olga Tapia, Miguel Lafarga, María T. Berciano, Jose M. Munuera, Silvia Villar-Rodil, Juan I. Paredes, María J. Rivero, Nazely Diban & Ane Urtiaga
Composites of polymer and graphene-based nanomaterials (GBNs) combine easy processing onto porous 3D membrane geometries due to the polymer and cellular differentiation stimuli due to GBNs fillers. Aiming to step forward to the clinical application of polymer/GBNs composites, this study performs a systematic and detailed comparative analysis of the influence of the properties of four different GBNs: (i) graphene oxide obtained from graphite chemically processes (GO); (ii) reduced graphene oxide (rGO); (iii) multilayered graphene produced by mechanical exfoliation method (Gmec); and (iv) low-oxidized graphene via anodic exfoliation (Ganodic); dispersed in polycaprolactone (PCL) porous membranes to induce astrocytic differentiation. PCL/GBN flat membranes were fabricated by phase inversion technique and broadly characterized in morphology and topography, chemical structure, hydrophilicity, protein adsorption, and electrical properties. Cellular assays with rat C6 glioma cells, as model for cell-specific astrocytes, were performed. Remarkably, low GBN loading (0.67 wt%) caused an important difference in the response of the C6 differentiation among PCL/GBN membranes. PCL/rGO and PCL/GO membranes presented the highest biomolecule markers for astrocyte differentiation. Our results pointed to the chemical structural defects in rGO and GO nanomaterials and the protein adsorption mechanisms as the most plausible cause conferring distinctive properties to PCL/GBN membranes for the promotion of astrocytic differentiation. Overall, our systematic comparative study provides generalizable conclusions and new evidences to discern the role of GBNs features for future research on 3D PCL/graphene composite hollow fiber membranes for in vitro neural models.
GBNs, graphene, POL/GBN