Calcium Chloride-Assisted Segregation Reduction of Chromite: Influence of Reductant Type and the Mechanism
Bibliographic record
Abstract
This paper describes an innovative process for the production of ferrochromium alloy via segregation reduction of chromite at 1300 °C in the presence of calcium chloride. Both charcoal and petroleum coke were used as the reductant. Individual polycrystalline ferrochrome carbide particles were produced, with their particle size and shape resembling that of the starting carbon particles. Interactions among calcium chloride, clinochlore, and chromite resulted in the formation of Ca-bearing chromite, wadalite, and gaseous chlorides. Monocrystalline ferrochrome carbide whiskers were formed only when charcoal in the presence of calcium chloride was used to reduce the chromite fraction containing large amounts of siliceous gangue. Incorporating thermodynamic evaluations, a possible segregation reduction mechanism is suggested based on the characterization of the products using scanning electron microscopy, energy dispersive spectroscopy, X-ray powder diffraction, and electron probe microanalysis, combined with thermogravimetric analysis. Metallization within chromite particles was minimized, which is suggested to be due to the ubiquitous presence of molten and gaseous calcium chloride between the chromite and carbon particles, and especially in the porous rim of the chromite particles during reduction, resulting in restricted metallization only on the carbon particles.
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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".