Advancing mechanical and biological characteristics of polymer-ceramic nanocomposite scaffolds for sport injuries and bone tissue engineering: A comprehensive investigation applying finite element analysis and artificial neural network

Abstract

In recent years, the application of polymer-ceramic nanocomposite scaffolds in bone tissue engineering has received considerable attention due to their structural similarity to natural bone tissue. Polycaprolactone (PCL) has emerged as a viable material for the fabrication of porous bone scaffolds. Composites that incorporate PCL with ceramic phases, such as nanocrystalline hydroxyapatite (n-HA) and tricalcium phosphate (TCP), have shown promise in promoting bone formation. Nevertheless, the use of bone scaffolds with complex geometries that mimic human bone poses challenges regarding their mechanical properties, which is the primary focus of this study. To assess the mechanical behavior of triangular nanostructures, particularly their ultimate compressive strength, finite element analysis (FEA) and artificial neural network (ANN) techniques were utilized. The obtained results were compared to experimental and analytical data. Three samples with varying weight percentages (0.1, 0.2, and 0.3) of HA and TCP nanoparticles embedded in PCL polymer were fabricated using a 3D fused deposition modeling technique. Scanning electron microscope (SEM) analysis was conducted to evaluate the morphology, while apatite formation rate and weight loss in simulated body fluid (SBF) and phosphate buffer saline (PBS) solution were assessed. The results revealed that a porosity of 76 % increases the apatite formation and dissolution rates by 23 % and 39 %, respectively. The SEM images, in conjunction with the simulated FEA models, indicated that scaffolds containing 0.3 wt% TCP nanoparticles exhibited favorable mechanical and biological properties for bone fracture applications. Additionally, the influence of different weight percentages of TCP and HA on the mechanical properties of the scaffolds was investigated using ANN. A neural network model was developed by incorporating 0.2 of each additive within a range of 0.1–0.3 while evaluating output data including elastic modulus, compressive strength, tensile strength, and Poisson's ratio. The predicted mechanical properties of the porous scaffold were subsequently analyzed and discussed.