Strengthening of Dense Bioceramic Samples Using Bioresorbable Polymers – A Statistical Approach
Bibliographic record
Abstract
Mechanical properties of bioceramics are poor and need to be improved for biomedical applications. In order to do this, bioceramics may be strengthened by bioresorbable polymers. In this study, the mechanical properties of poly(ε-caprolactone), PCL, coated dense bioceramic pellets made of silica-contained calcium phosphates were studied and analyzed using a statistical experimental design in conjunction with Taguchi methods for optimization. The aim of this experimental work was to maximize the pellet flexural strength and minimize the amount of deposited PCL. The most important factors affecting the strengthening of the ceramic pellets were evaluated. Four independent processing variables (a removal technique of an excess polymer solution, concentration of PCL in the solution, a heat treatment temperature and the number of dipping) with three levels of variability were tested using an L9 (34) orthogonal array. A statistical experimental design using the analysis of means and orthogonal array was applied to optimize the responses of these variables. The optimal conditions for achieving the maximal flexural strength of the coated pellets at the minimal amount of the deposited PCL were determined. A high quality dense bioceramic pellets with ~ 10.5 MPa flexural strength and ~ 80 μm thickness (~ 21 mg weight) of the deposited PCL coating were manufactured as a result.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".