A Parametric Study on the Processing and Physical Characterization of PLGA 50/50 Bioscaffolds
Bibliographic record
Abstract
The ability to control the characteristics of scaffolds is important such that scaffolds can be fine tuned for specific applications. In this study, the effects of processing parameters on cell morphology and mechanical properties of PLGA 50/50 bioscaffolds for tissue engineering applications were investigated. Specifically, the effects of salt particle sizes and salt-to-polymer mass ratios on the scaffold relative density, average pore size and density, open-cell porosity, and mechanical properties were examined. The PLGA samples were processed using a salt leaching technique in a batch-foaming setup. Experiments showed that pore size and density were dependent on the salt particle size used, and that as the salt-to-polymer mass ratio increased, the porosity increased while the relative density decreased. The results showed that by varying the salt parameters during fabrication, the scaffold characteristics and morphology can be controlled.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".