Microstructure and mechanical properties of silica hydrogels from sodium silicate solutions
Bibliographic record
Abstract
Silica hydrogels from sodium silicate solutions show potential for composite bone scaffolds, but their mechanical properties are not fully understood. This study investigates how pH, waterglass concentration, and acid initiators affect the mechanical properties and microstructure of silica hydrogels. Real-time compression tests revealed three stress–strain behaviors: brittle fracture, plastic deformation with a plateau, and continuous strain hardening. Initial compressive strengths ranged from 7.3 to 9.9 MPa, increasing to 16–38 MPa (basic group) and 21–53 MPa (acidic group) after aging. Basic 1:3 (25 wt% waterglass) and acidic 1:1 (50 wt% waterglass) formulations exhibited the highest strengths in their group. Engineering stress overestimated true stress by 21–66 %. Correlating the gradient stress–strain curves with real-time observations of deformation stages under compression revealed the connection between critical transitions in the stress–strain curve and stages such as crack initiation, propagation, and fragmentation. Structurally, basic gels formed larger structural units and pores (3–5 µm), contributing to increased ductility, while acidic gels formed a denser network with smaller pores, contributing to brittle behavior. Dilution increased pore size to 15–20 µm in both groups. These findings provide valuable insights for optimizing the processing-structure–property relationship in silica hydrogels for composite 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.
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".