A novel process for the treatment of steelmaking converter dust: Selective leaching and recovery of zinc sulfate and synthesis of iron oxides@HTCC photocatalysts by carbonizing carbohydrates
Bibliographic record
Abstract
A large amount of converter dust (CD) is generated annually, posing serious environmental problems due to its zinc content. Moreover, zinc mainly exists in the form of inert ZnFe 2 O 4 in CD, which negatively affects the leaching efficiency of zinc. On the other hand, the photocatalytic activity of hydrothermal carbonation carbon (HTCC) is restricted by its wide bandgap, poor charge-transfer ability, and inconvenience of recycling. This work presents an oxygen pressure leaching method to selectively leach Zn from CD. In this process, not only the decomposition of ZnFe 2 O 4 is effectively promoted to increase zinc recovery, but also Fe 2+ in the solution is skillfully oxidized and hydrolyzed to solid Fe( OH )SO 4 . Under the optimum conditions, the leaching efficiency of zinc reached 98.8%, and 86.3% of iron remained in the leach residue, realizing the one-step separation of zinc and iron. Furthermore, the leach residue was successfully applied to synthesize a magnetically recyclable iron oxides@HTCC composite photocatalyst .
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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) | 0.000 | 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".