Interfacial Reaction between Refractory Materials and Metallurgical Slags containing Fluoride
Bibliographic record
Abstract
Abstract Interfacial reaction between refractory materials such as zirconia, magnesia and doloma brick, and the metallurgical slags of the CaO‐SiO 2 ‐MgO‐CaF 2 system with varying CaF 2 content were investigated at high temperatures using various methodologies with static and dynamic modes. To figure out the corrosion mechanism due to interfacial reaction with the slag, the slag characteristics were examined in terms of flow temperature and viscosity and the corroded interface of zirconia, magnesia and doloma refractories were analyzed by SEM‐EDS and EPMA. With an addition of CaF 2 , three different layers were formed at the interface between slag and zirconia refractory. Furthermore, the corrosion of zirconia refractory was found to be accelerated with an increase of CaF 2 which facilitated the dissolution of intermediate compounds. The penetration of slag through the grain boundaries of MgO refractory is enhanced by increasing the content of CaF 2 due to an increase in the fluidity of slag in the dynamic mode. On the other hand, in the static condition, a dense Ca 2 SiO 4 layer is formed at the hot face of magnesia‐doloma refractory due to a reaction between silica in slag and lime in doloma, resulting in the protection of direct corrosion of refractory brick. However, the thickness of C 2 S layer decreases with increasing content of CaF 2 due to an increase in fluidity of slag.
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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".