Structural connectivity and bioactivity in sol–gel silicate glass design
Bibliographic record
Abstract
Bioactive glasses (BGs) bond with bone by forming hydroxy carbonate apatite (HCA) upon reaction in physiological fluid, a phenomenon known as bioactivity. BGs structural network connectivity determines their bioactivity. Sol–gel BGs are synthesized through the hydrolysis and condensation of metal alkoxide precursors in the presence of a catalyst, in aqueous environments. Several sol–gel synthesis parameters directly impact BG network connectivity: pH (i.e. acid or basic catalysis), water to alkoxide ratio (R w ), alkoxide type and presence of dopant ions. However, the relationship between bioactivity and these parameters remains surprisingly unexplored. This study highlights the relationship between synthesis pH, R w , network connectivity and bioactivity in silica-based sol-gel BGs and BGs doped with titanium (Ti) ions (TiBGs), the latter selected for their known ability to enhance network connectivity. BGs and TiBGs are synthesized with various R w values under acidic and basic conditions, and their bioactivity is assessed in simulated body fluid for 7 days. Increasing R w decreases network connectivity and increases bioactivity of BGs with high network connectivity, as observed for base-catalyzed BGs and for both acid and base catalyzed TiBGs, but not in BGs with lower connectivity as evidenced in acid-catalyzed BGs. Basic catalysis of TiBGs prevents crystalline TiO 2 domain formation, which was instead consistently observed in TiBGs synthesized under acidic catalysis. These findings help the design of BGs for applications where ion release needs to be enhanced even in the presence of dopants that slow down HCA formation, and of BGs with specific properties, e.g. TiO 2 -containing BGs with potential bactericidal activity. Bioactive glasses (BGs) bond with bone by dissolving and forming hydroxycarbonate apatite (HCA) on their surface, offering applications in medicine and dentistry. BG's network connectivity influences its dissolution rate, and hence HCA formation. While solution-gelation (sol-gel) is commonly used for BG production, the effect of sol gel synthesis parameters on HCA formation remains unexplored. We studied the relationship between synthesis parameters (water-to-alkoxide ratio (R w ), catalyst, and dopant ions, particularly titanium), BG network connectivity, and HCA formation. We find that increasing R w with any catalyst enhances HCA formation, particularly in glasses with high network connectivity. This understanding allows tailoring BG synthesis for different applications, e.g. those requiring doping with ions that increase network connectivity and fills a crucial gap in BG literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".