Process Mapping of the Sol–Gel Transition in Acid-Initiated Sodium Silicate Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it