Transfer Behavior of Fe Element in Nickel Slag during Molten Oxidation and Magnetic Separation Processes
Bibliographic record
Abstract
High-percentage iron resources in nickel slags were recovered as magnetite via molten oxidation process, and the transfer behavior of Fe element was studied. The elemental distribution in oxidized slag samples, the influence of atmosphere, holding temperature and time on magnetite crystal growth, and Fe element distribution in magnetic materials were also investigated. It was found that magnetite could be produced from fayalite or hortonolite in nickel slags during molten oxidation with CaO as a modifier, air as an oxidizer, accompanying with the enrichment of Fe, Co, Ni and Cu. The select of atmosphere is very important during the precipitation and growth of the magnetite crystals. The magnetite crystals precipitated invisibly or slightly in argon atmosphere, while exhibited dendritic structures with crystallization content of ∼18.5% in air atmosphere. Especially, after blowing air into molten slag for 30 min, magnetite crystals develop well-distributed and complete, resulting in its crystallization content increases up to 33.5%. The Fe content in the matrix of oxidized samples remained approximately constant after holding for 20 min. Mössbauer spectra analysis indicates that the 89.6% of Fe exists in magnetite phases, while only 10.4% of Fe in hedenbergite. It was also found that Ni and Co simultaneously concentrate in the magnetite phase, indicating that Fe, Ni, and Co can be recovered effectively from nickel 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".