Mechanical characterization of pore-graded bioactive glass scaffolds produced by robocasting
Bibliographic record
Abstract
Abstract Since the discovery of 45S5 Bioglass® by Larry Hench, bioactive glasses have been widely studied as bone substitute materials and, in more recent years, have also shown great promise for producing three-dimensional scaffolds. The development of additive manufacturing techniques and their application in bone tissue engineering allows the design and fabrication of complex structures with controlled porosity. However, achieving strong and mechanically-reliable bioactive glass scaffolds is still a great challenge. Furthermore, there is a relative paucity of studies reporting an exhaustive assessment of other mechanical properties than compressive strength of glass-derived scaffolds. This research work aimed at determining key mechanical properties of silicate SiO2-Na2O-K2OMgO-CaO-P2O5 glass scaffolds fabricated by robocasting and exhibiting a porosity gradient. When tested in compression, these scaffolds had a strength of 6 MPa, a Young’s modulus around 340 MPa, a fracture energy of 93 kJ/m3 and a Weibull modulus of 3, which provides a quantification of the scaffold reliability and reproducibility. Robocasting was a suitable manufacturing method to obtain structures with favorable porosity and mechanical properties comparable to those of the human cancellous bone, which is fundamental regarding osteointegration of bone implants.
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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 | 0.001 | 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".