Bibliographic record
Abstract
Abstract The remelting of automobile scrap, containing galvanized steel, in an electric arc furnace (EAF) results in the generation of a dust, which contains considerable amounts of zinc and other metals. Typically, the amount of zinc is of significant commercial value, but the recovery of this metal can be hindered by the varied speciation of zinc. The majority of the zinc exists as zincite (ZnO) and zinc ferrite (ZnFe 2 O 4 ) or ferritic spinels ((Zn x Mn y Fe 1– x–y )Fe 2 O 4 ), but other zinccontaining species such as zinc chloride, zinc hydroxide chlorides, hydrated zinc sulphates and zinc silicates have also been identified. There is a scarcity of research literature on the thermodynamic aspects of the formation of these zinc-containing species, in particular, the minor zinc-containing species. Therefore, in this study, the equilibrium module of HSC Chemistry ® 6.1 was utilized to calculate the types and the amounts of the zinc-containing species. The variables studied were: the gas composition, the temperature and the dust composition. At high temperatures, zincite forms via the reaction of zinc vapour with oxygen gas and the zinc-manganese ferrites form as a result of the reaction of iron-manganese particles with zinc vapour and oxygen. At intermediate temperatures, zinc sulphates are produced through the reaction of zinc oxide and sulphur dioxide gas. As room temperature is approached, zinc chlorides and fluorides form by the reaction of zinc oxide with hydrogen chloride and hydrogen fluoride gases, respectively. Zinc silicate likely forms via the high temperature reaction of zinc vapour and oxygen with silica. In the presence of excess water and as room temperature is approached, the zinc sulphates, chlorides and fluorides can become hydrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".