Physical, mechanical characterization, and artificial neural network modeling of biodegradable composite scaffold for biomedical applications

Abstract

The performance of substitute osteoconductive scaffolds in guiding new bone formation and creating vital biological conditions in living organisms is of crucial importance. In this study, bioresorbable scaffolds were synthesized by incorporating polyvinyl alcohol (PVA) with hydroxyapatite nanoparticles (n-HAP) and Iron oxide (Fe3O4) nanoparticles (NPs) (MNPs) using a freeze-drying technique. Subsequently, the magnetic nanocomposite scaffolds were immersed in a phosphate-buffered saline (PBS) solution for 24 days to assess their degradability percentage. The physicochemical and morphological properties of the magnetic nanocomposite scaffolds were evaluated using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Furthermore, the mechanical behavior was examined, while the pore dimensions and porosity were determined using immersion-based techniques. Noteworthy attributes of the magnetic nanocomposite scaffolds were observed, including a maximum swelling capacity of 176 %, a substantial porosity of 72.7%, and impressive mechanical characteristics, such as a compressive strength of 4.61 MPa and an elastic modulus of 2.13 GPa. The degradation study conducted in a PBS solution revealed that the scaffold with the highest n-HAP content (20 wt%) exhibited a degradation rate of approximately 21% after a 24-day period. The influence of environmental conditions, including time, temperature, and salt-containing environments, on the swelling behavior and bio-resorption of the scaffolds was examined. Additionally, an artificial neural network (ANN) was developed to forecast the impact of weight percentages of n-HAP and MNPs on various scaffold properties. According to the ANN predictions, increasing the weight percentages of n-HAP and MNPs resulted in a reduction in pore size, an increase in porosity, and an improvement in the mechanical properties of the scaffolds. Moreover, the incorporation of a higher number of nanoparticles led to increased absorption and swelling, facilitating bone tissue formation. Satisfactory accuracy in predicting scaffold properties was demonstrated through error analysis and linear regression plots, aiding designers in selecting appropriate weight percentages of nanoparticles for future designs. The findings indicate that the synthesized magnetic nanocomposite scaffold closely resembles the physical and mechanical characteristics of natural bone tissue. The utilization of an ANN for performance assessment demonstrated favorable efficiency and effectiveness. Therefore, the significance of the current study lies in the comprehensive investigation of the synthesis and characterization of a biodegradable magnetic nanocomposite scaffold, as well as the utilization of an ANN-based approach to predict and optimize its properties for potential bone tissue engineering applications.