Toughening of porous bioceramic scaffolds by bioresorbable polymeric coatings
Bibliographic record
Abstract
The mechanical properties of poly(c-caprolactone) (PCL)-coated porous bioceramic scaffolds made of calcium phosphates were studied and analysed using a statistical experimental design and Taguchi methods. In this study, both the flexural strength of the coated scaffolds and the amount of deposited PCL were considered as the measured responses. A statistical experimental design using the analysis of means and orthogonal array was applied to optimize these responses. The removal technique of excess polymer solution, the concentration of PCL in the solution, a heat treatment temperature, and the number of times that the scaffolds were dipped in the solution were chosen as the significant processing variables. The removal technique of excess polymer solution and the number of times that the scaffolds were dipped in the solution showed the major effects on the flexural strength, while the technique for removal of excess polymer solution was found to have the major effect on the mass of the deposited PCL. The optimal conditions for achieving the maximal flexural strength of the coated scaffolds at the minimal amount of the deposited PCL were determined and tested. High-quality porous bioresorbable scaffolds with approximately 19 MPa flexural strength and approximately 0.4g of the total (PCL coating+calcium phosphates) mass with dimensions of 8.5 mm diameter and 13 mm width 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".