Material model measurements and predictions for a random pore poly(epsilon-caprolactone) scaffold

Area of Science:

• Biomaterials Engineering
• Polymer Science
• Computational Mechanics

Background:

• Polymeric scaffolds are crucial for bone tissue engineering.
• Developing accurate material models is essential for predicting scaffold performance.
• Poly(epsilon-caprolactone) (PCL) and poly(ethylene oxide) (PEO) are commonly used biodegradable polymers.

Purpose of the Study:

• To investigate and develop material models for porous polymeric bone scaffolds.
• To establish a link between scaffold microstructure and bulk mechanical properties.
• To provide tools for designers for quasi-static analysis and quality control.

Main Methods:

• Co-extrusion of PCL and PEO to create porous scaffolds.
• Fitting stress-strain compression data with a hyperbolic phenomenological model.
• Utilizing finite element analysis (FEA) based on microstructural images.
• Predicting elastic modulus using a "brick" finite element approach.

Main Results:

• A hyperbolic material model was developed for quasi-static analysis.
• The secant modulus at 10% strain showed less scatter than hyperbolic model coefficients.
• FEA models based on microstructure accurately predicted the small-strain elastic modulus within one standard deviation.
• Scaffold bulk properties can be predicted from constituent material properties and microstructure.

Conclusions:

• The developed material models are valuable for designing and analyzing polymeric bone scaffolds.
• Microstructure-based FEA provides a reliable method for predicting scaffold mechanical properties.
• This approach facilitates scaffold design optimization and quality assurance in biomaterial development.