Study of the mechanical stability and bioactivity of Bioglass <sup>®</sup> based glass-ceramic scaffolds produced via powder metallurgy-inspired technology
Bibliographic record
Abstract
Large bone defects are challenging to heal, and often require an osteoconductive and stable support to help the repair of damaged tissue. Bioglass-based scaffolds are particularly promising for this purpose due to their ability to stimulate bone regeneration. However, processing technologies adopted so far do not allow for the synthesis of scaffolds with suitable mechanical properties. Also, conventional sintering processes result in glass de-vitrification, which generates concerns about bioactivity. In this work, we studied the bioactivity and the mechanical properties of Bioglass(®) based scaffolds, produced via a powder technology inspired process. The scaffolds showed compressive strengths in the range of 5-40 MPa, i.e. in the upper range of values reported so far for these materials, had tunable porosity, in the range between 55 and 77%, and pore sizes that are optimal for bone tissue regeneration (100-500 μm). We immersed the scaffolds in simulated body fluid (SBF) for 28 d and analyzed the evolution of the scaffold mechanical properties and microstructure. Even if, after sintering, partial de-vitrification occurred, immersion in SBF caused ion release and the formation of a Ca-P coating within 2 d, which reached a thickness of 10-15 μm after 28 d. This coating contained both hydroxyapatite and an amorphous background, indicating microstructural amorphization of the base material. Scaffolds retained a good compressive strength and structural integrity also after 28 d of immersion (6 MPa compressive strength). The decrease in mechanical properties was mainly related to the increase in porosity, caused by its dissolution, rather than to the amorphization process and the formation of a Ca-P coating. These results suggest that Bioglass(®) based scaffolds produced via powder metallurgy-inspired technique are excellent candidates for bone regeneration applications.
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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".