Physical and Mechanical Properties of Poly(E-Caprolactone)–Hydroxyapatite Composites for Bone Tissue Engineering Applications
Bibliographic record
Abstract
Tissue engineering using bioscaffolds is a promising technique that may provide new treatments for various diseases and injuries. These bioscaffolds can be temporary or permanent materials that can be implanted in a patient for tissue repair. Poly(ε-caprolactone) (PCL) is a biocompatible and biodegradable polymer, and hence suitable for usage in tissue engineering applications. Depending on the targeted tissue to be repaired, scaffold properties need to be altered to match that of the tissue. In load bearing applications, such as bone repair, the mechanical properties need to be sufficiently high to prevent material failure. To strengthen the scaffold, various composites have been proposed in the literature, and one of these composites includes PCL with hydroxyapatite (HA). To be able to control the processing of these materials into scaffolds, the characterization of fundamental material properties need to be investigated. In this study, the physical, thermal, mechanical, and viscoelastic properties of PCL:HA at three different weight compositions of 80:20, 70:30, and 60:40 wt% were characterized and compared to neat PCL. PCL/HA composites were fabricated by blending using a twin-screw compounder, and disc-shaped samples were fabricated by compression molding at an elevated temperature. Analysis using a differential scanning calorimeter demonstrated that the glass transition and melting temperatures of the composites remained nearly unaffected by the HA content at −56 °C and 56 °C, respectively; however, depending on the cooling method used for processing, the degree of crystallinity can be controlled. Thermogravimetric analysis was also performed to study the thermal degradation profile. PCL-HA composite samples were tested in compression to determine the effects of HA content on the mechanical properties. Compared to neat PCL, incorporating HA at 40 wt% increased the modulus nearly twofold from 85 to 155 MPa. Lastly, to study the viscoelastic properties of the solid materias, frequency dependency and creep experiments were performed using a dynamic mechanical analyzer. The composites at high HA concentrations were more compliant to creep and other viscoelastic effects. The results found in this study are important in developing novel processing techniques or scaffolds and in controlling final scaffold properties such that any desired properties may be fabricated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".