Effect of Slag Composition on Wettability of Oxide Inclusions
Bibliographic record
Abstract
Inclusion removal is key in the production of high quality steel. The inclusions are primarily removed from liquid steel by reacting with a liquid slag phase. For efficient inclusion removal, the inclusions transfer across the steel-slag interface to dissolve in the slag. This transfer process is strongly influenced by interfacial phenomena. In this study, the dynamic wetting (θ) of a range of slags in the CaO–Al2O3–SiO2–(MgO) system on solid oxides representing inclusion phases (Al2O3, MgAl2O4 and CaO.Al2O3) at 1773 K was investigated using a sessile drop technique. It was found that for all systems studied θ versus time showed a rapid decrease in wetting in the first 10 s tending to a plateau value at extended times. Further, for basic type ladle slags the plateau value was independent of slag composition and for acid type tundish slags the plateau value decreased with increasing basicity. Through work of adhesion analysis it was shown that ladle type slags appeared more suitable for inclusion removal and that from a wetting perspective calcium aluminate would be easier to remove than spinel and alumina. Choi and Lee’s dynamic wetting model was evaluated and found to not only represent the data well but have physical relevance for the basic, but not the acid, slags investigated.
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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".