Development of cordierite-based glass-ceramics by slip casting through selecting the appropriate sintering conditions
Bibliographic record
Abstract
The present work aims to prepare a dense cordierite-based glass-ceramic through slip casting and consequent heat treatment procedures. In this regard, sintering conditions were considered as the key variables to improve the properties of the glass-ceramic. For this purpose, glass frit powder was prepared through melting oxide powders (in the system of SiO2-Al2O3-TiO2-K2O-CaO-MgO). The mixed powders were then heat treated at 1450 °C for 1 hour and quenched in water. The glass frit powder was slip cast using the appropriate dispersant. Sintering was carried out by one-step, two-step, and three-step procedures. Specimens were characterized in terms of various analysis techniques including dilatometry, X-ray diffractometry, scanning electron microscopy, and mechanical strength measurement. Among the examined specimens, the sample sintered by a three-step approach was considered the optimized one which attained zero porosity. According to the obtained results, cordierite crystals were observable in this glass-ceramic matrix. A low coefficient of thermal expansion and a low dielectric constant were observed for the optimized glass-ceramic sample. The obtained results confirmed that the homogenous distributions of crystalline phases are responsible for the appropriate and desirable properties of the prepared glass-ceramic.
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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 | 0.001 |
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".