Effects of Heat Treatment System on Mechanical Strength and Crystallinity of CaO-MgO- Al2O3-SiO2 Glass-Ceramics Containing Coal Gangue and Iron Ore Tailings
Bibliographic record
Abstract
For comprehensive utilization of solid wastes, the CaO-MgO-Al 2 O 3 -SiO 2 glass-ceramics have excellent mechanical properties were prepared the glass-ceramics were prepared with coal gangue and iron ore tailings by a modified melting method.Heat treatment consists of nucleation and crystallization, the best heat treatment in the experiment of coal gangue and iron ore tailings is as followed: nucleation temperature 780 o C and crystallization temperature 980 o C, the bending strength is up to 283.3 MPa.X-ray diffraction (XRD) shows that crystalline phase is augite, and the mechanical strength of coal-gangue/iron-ore-tailings glass-ceramics becomes higher as the crysallinity increased.While anorthite and diopside become the crystalline phase, the whole strength greatly decreased.Differential scanning calorimetry (DSC) curve and bending strength curves indicate that, the optimum nucleation temperature is 38 o C higher than glass transition temperature, meanwhile the best crystallization temperature is 68 o C higher that the exothermic one.Scanning electron microscope (SEM) and transmission electron microscopy (TEM) demonstrate that there are two kinds of crystalline phase: one is disordered and arranged closed and other pearl chain-like.Both of arrangements are performed as 200 nm ~ 300 nm grains composed of 40 nm~60 nm submicroscopic particulate, which contributed to the high mechanical strength of coal-gangue/iron-ore-tailings glass-ceramics.
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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".