Predicting permeability of regular tissue engineering scaffolds: scaling analysis of pore architecture, scaffold length, and fluid flow rate effects
Bibliographic record
Abstract
The main aim of this research is to numerically obtain the permeability coefficient in the cylindrical scaffolds. For this purpose, a mathematical analysis was performed to derive an equation for desired porosity in terms of morphological parameters. Then, the considered cylindrical geometries were modeled and the permeability coefficient was calculated according to the velocity and pressure drop values based on the Darcy's law. In order to validate the accuracy of the present numerical solution, the obtained permeability coefficient was compared with the published experimental data. It was observed that this model can predict permeability with the utmost accuracy. Then, the effect of geometrical parameters including porosity, scaffold pore structure, unit cell size, and length of the scaffolds as well as entrance mass flow rate on the permeability of porous structures was studied. Furthermore, a parametric study with scaling laws analysis of sample length and mass flow rate effects on the permeability showed good fit to the obtained data. It can be concluded that the sensitivity of permeability is more noticeable at higher porosities. The present approach can be used to characterize and optimize the scaffold microstructure due to the necessity of cell growth and transferring considerations.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".