Titanium-Containing Silicate-Based Sol–Gel Bioactive Glass: Development, Characterization, and Applications
Bibliographic record
Abstract
Bioactive glasses are surface-reactive glasses that, when placed in physiological fluid, undergo a transformation from glass to hydroxyapatite. Doping the bioactive glass with metallic ions can impart desirable and unique properties that are not inherent to natural hydroxyapatite. Once such ion is titanium. Titanium exists in trace amounts in native dental enamel, and its presence has been correlated with increased tooth hardness and brightness, both desirable clinical properties. Synthetic titanium-substituted hydroxyapatite exhibits better mechanical and antibacterial properties and demonstrates potential for an improved cellular response when compared to unmodified hydroxyapatite with applications in the broader field of bone tissue engineering. In this work, we use the sol-gel method to synthesize a titanium-containing silicate-based bioactive glass aimed at generating titanium-substituted hydroxyapatite on the glass surface upon immersion in body fluid. Titanium is homogeneously distributed throughout our glass, which keeps its amorphous nature. After 14 days of immersion in simulated body fluid, the glass forms a titanium-substituted hydroxyapatite on its surface. Enamel surfaces treated with the titanium-containing glass show significantly increased microhardness compared to enamel surfaces treated with a control glass, confirming the potential for the proposed glass in enamel remineralization. We also show that the presence of titanium in the glass promotes cell differentiation toward bone formation, suggesting further applications for this material in the broader field of bone tissue engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".