Aging Time and Temperature Effects on the Structure and Bioactivity of Gel‐Derived 45S5 Glass‐Ceramics
Bibliographic record
Abstract
Porous bioactive glass‐ceramics based on the 45S5 Bioglass ® composition were fabricated by an acid‐catalyzed sol–gel method. The effects of aging time and temperature on the structure and in vitro bioactivity were investigated. Fourier‐transform infrared spectroscopy ( FTIR ) was carried out on the samples to understand the structure and to monitor the formation of hydroxyapatite ( HA ) after immersion in simulated body fluid ( SBF ). The bioactivity of gel‐derived 45S5 glass‐ceramic and amorphous 45S5 Bioglass ® was compared. The results showed that an increase in both aging time and temperature can enhance crystallization, whereas bioactivity is reduced with increasing aging time but not significantly influenced by aging temperature. Compared with amorphous 45S5 Bioglass ® , gel‐derived glass‐ceramic aged for 3 d at 60°C exhibited a more rapid rate of HA formation after immersion for less than 7 d. Amorphous 45S5 Bioglass ® showed higher HA formation rate after immersion in SBF for more than 7 d, whereas the quantity of formed HA on gel‐derived 45S5 glass‐ceramic was still comparable to that of amorphous 45S5 Bioglass ® after immersion for 14 d. It is suggested that the lower bioactivity of 45S5 glass‐ceramics could be outweighed by the higher surface area and higher content of Si – NBO groups in gel‐derived glass‐ceramics. The results thus confirm that gel‐derived 45S5 glass‐ceramic exhibiting bioactivity comparable to that of amorphous 45S5 Bioglass ® can be fabricated by sol–gel method under suitable aging conditions.
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 | 0.001 |
| 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".