Synergetic effect of bioglass and nano montmorillonite on 3D printed nanocomposite of polycaprolactone/gelatin in the fabrication of bone scaffolds.

ABSTRACT

Nowadays, bone injuries and disorders have increased all over the world and can reduce the quality of human life. Bone tissue engineering repair approaches require new biomaterials and methods to construct scaffolds with the required structural properties as well as improved performance. As potential therapeutic strategies in bone tissue engineering, 3D printed scaffolds have been developed. Polycaprolactone/Ceramic composites have attracted considerable attention due to their cytocompatibility, biodegradability, and physical properties. In this study, a 3D printing process was used to create polycaprolactone (PCL)-Gelatin (GEL) scaffolds containing varying concentrations of Bioglass (BG) and Nano Montmorillonite (MMT). This mixture was then loaded into a 3D printer, and the scaffolds were printed layer by layer. After constructing the scaffolds, they were then examined for their physical, chemical, and biological characteristics. Surface appearance was analyzed with a scanning electron microscope (SEM), which revealed that NC increased the diameter of pores from 465 to 480 µm. The elements in the scaffolds were evaluated by EDX analysis, and a uniform dispersion of nano montmorillonite particles was observed. The compressive strength reached 76.43 MPa for PCL/G/35 %MMT/15 %BG scaffold. Also, the rate of water absorption, biodegradability and bioactivity of PCL-GEL scaffolds increased significantly in the presence of NC. According to the MTT cell test results, adding BG and NC increased cell proliferation, adhesion and cell viability to 127.7 %. These findings indicated that the 3D printed PCL/G/35 %MMT/15 %BG scaffold has promising strategies for bone repair applications. Also, polynomial curve fitting shows that scaffold degradability after soaking in PBS can be predicted using the initial weight and soaking time. Adding more variables and data could improve prediction accuracy, reducing the need for experiments and conserving resources.