Leachability of nitrided ilmenite in hydrochloric acid
Bibliographic record
Abstract
Titanium nitride in upgraded nitrided ilmenite (bulk of iron \nremoved) can selectively be chlorinated to produce titanium \ntetrachloride. Except for iron, most other components present \nduring this low temperature (ca. 200°C) chlorination reaction will \nnot react with chlorine. It is therefore necessary to remove as much \niron as possible from the nitrided ilmenite. Hydrochloric acid \nleaching is a possible process route to remove metallic iron from \nnitrided ilmenite without excessive dissolution of species like \ntitanium nitride and calcium oxide. Calcium oxide dissolution \nresults in unrecoverable acid consumption. The leachability of \nnitrided ilmenite in hydrochloric acid was evaluated by determining \nthe dissolution of species like aluminium, calcium, titanium and \nmagnesium in a batch leach reactor for 60 minutes at 90°C under \nreflux conditions. The hydrochloric acid concentration (11%, 18% \nand 25%), initial acid-to-iron mole ratio (2:1, 2.5:1 and 3.3:1), and \nsolid-to-liquid mass ratio (1:8.33 to 1:2.13) were varied. The results \nindicate that a hydrochloric acid concentration of 25 wt% supplied \nin a 2:1 acid-to-iron mole ratio would produce the most favourable \nupgraded nitrided ilmenite product. The dissolution of iron in this \nsolution reached 97 per cent after only 60 minutes. The total \ndissolution of calcium and titanium species was 0.01 and 0.11 wt% \nrespectively. Hydrochloric acid can therefore be used as lixiviant to \nremove metallic iron from nitrided ilmenite.
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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".