Process Mapping of the Sol–Gel Transition in Acid-Initiated Sodium Silicate Solutions
Bibliographic record
Abstract
Fabricating large-scale porous bioactive glass bone scaffolds presents significant challenges. This study aims to develop formable, in situ setting scaffolds with a practical gelation time of about 10 min by mixing 45S5 bioactive glass with sodium silicate (waterglass) and an acid initiator. The effects of pH (2-11), waterglass concentration (15-50 wt.%), and acid initiator type (phosphoric or boric acid) were examined to optimize gelation kinetics and microstructure. A 10 min gelation time was achieved with boric acid and phosphoric acid at various pH levels by adjusting the waterglass concentration. Exponential and polynomial models were proposed to predict gelation times in basic and acidic environments, respectively. The optical properties of the gels were studied qualitatively and quantitatively, providing insights into gelation kinetics and structure. Acidic gels formed smaller particles in a dense network (pores < 550 nm) with higher light transmittance, while basic gels had larger aggregates (pores ~5 µm) and lower transmittance. As the waterglass concentration decreased, pore size and transmittance converged in both groups. The onset of gelation was detected around 8 min using the derivative of light transmittance. This work identifies the key factors controlling waterglass gelation and their impact on gel structure, enabling the tailored creation of formable, in situ setting bioactive glass bone scaffolds.
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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".