Morphology and degradation properties of PCL/HYAFF11® composite scaffolds with multi-scale degradation rate
Source abstract
Abstract
The analysis of scaffold degradation is a promising strategy for understanding the dynamic changes in texture and pore morphology which accompany polymer resorption, and for collecting same fundamental indicators regarding the potential fate of the scaffold in the biological environment. In this study, we investigate the morphology and degradation properties of three composite scaffolds based on poly(ε-caprolactone) (PCL) embedded with benzyl ester of hyaluronic acid (HYAFF11) phases, and, in turn, different reinforcement systems - i.e, calcium phosphate particles or continuous poly(lactic acid) (PLA) fibres. Scanning electron microscopy (SEM) and μ-tomography supported by digital image analysis enabled a not invasive investigation of the scaffold morphology, providing a quantitative assessment of porosity (which ranged from 63.1 to 82.8), pore sizes (which varied from 170.5 to 230.4 μm) and pore interconnectivity. Thermal analyses (DSC and TGA) and Raman spectroscopy demonstrated the multi-scale degradation of the composite with highly tailoring degradation kinetics depending on the component material phases and scaffold architecture changes, due to their conditioning in simulated environment (i.e, SBF solution). These results demonstrate that the judicious mixing of materials with faster (i.e, Hyaff11) and slower (i.e, PLA and PCL) degradation kinetics, different size and shape (i.e, domains, particles or long fibres), certainly concurs to design a smart composite scaffold with time-controlled degradation which can support the regeneration of a large variety of tissues, from the cartilage to the bone.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: biodegradable polymer synthesis and properties · Bone Tissue Engineering Materials · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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