Viscoelastic properties of poly(ε‐caprolactone) – hydroxyapatite micro‐ and nano‐composites
Bibliographic record
Abstract
Various composites have been proposed in the literature for the fabrication of bioscaffolds for bone tissue engineering. These materials include poly(ε‐caprolactone) (PCL) with hydroxyapatite (HA). Since the biomaterial acts as the medium that transfers mechanical signals from the body to the cells, the fundamental properties of the biomaterials should be characterized. Furthermore, in order to control the processing of these materials into scaffolds, the characterization of the fundamental properties is also necessary. In this study, the physical, thermal, mechanical, and viscoelastic properties of the PCL‐HA micro‐ and nano‐composites were characterized. Although the addition of filler particles increased the compressive modulus by up to 450%, the thermal and viscoelastic properties were unaffected. Furthermore, although the presence of water plasticized the polymer, the viscoelastic behavior was only minimally affected. Testing the composites under various conditions showed that the addition of HA can strengthen PCL without changing its viscoelastic response. The results found in this study can be used to further understand and approximate the time‐dependent behavior of scaffolds for bone tissue engineering. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".