The effect of pore size and layout on mechanical and biological properties of <scp>3D</scp>‐printed bone scaffolds with gradient porosity
Bibliographic record
Abstract
Abstract The design of porous gradient scaffolds for bone tissue engineering scaffolds is a relatively new approach. This strategy is based on imitating bone tissue in order to stimulate enhanced cellular responses. An additive manufacturing (AM) method, such as the fused filament fabrication (FFF) system, provides precise and repeatable pore size control. FFF is a well‐known AM manufacturing process for producing high‐quality parts at a low cost. In this study, polycaprolactone (PCL) and variable hydroxyapatite (HA) amounts were fed into a FFF printer to print four scaffold designs with different porosity gradients. These porous gradient scaffolds were constructed using simple (Si) and shifting (Sh) models, with gradient pore diameters ranging from 400–600 to 400–800 μm. The specimens featured thicker walls but more open cores. The scaffolds' structural, mechanical, and biological properties were evaluated. The results showed that higher gradient porosity and larger pore size led to better biological results, but lower mechanical strength resulted. Furthermore, adding HA increased mechanical strength from 81.8% to 100% and enhanced cellular response. In all scaffolds, an increase in porosity and a decrease in density led to a reduction in compressive strength. The toxicity of the samples and cellular adhesion was evaluated using MTT and DAPI tests on hFOB (human Fetal OsteoBlastic) cells. Alkaline phosphatase and Red Alizarin analyses demonstrated an increase in mineralization as HA content increased.
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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 | 0.000 |
| 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) | 0.001 | 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".