Evaluation of Contributions of Acid and Ligand to Ni, Co, and Fe Dissolution from Nonferrous Smelter Slags in Aqueous Sulfur Dioxide
Bibliographic record
Abstract
To assess the effectiveness of aqueous SO 2 in extracting metal values from discarded smelter slag, the dissolution behavior of Co, Ni, and Fe using SO 2 (aq) and H 2 SO 4 was investigated. Experiments were carried out in batch mode under ambient conditions, with an emphasis on the relative contribution of acid (H + ) and SO 2 (aq) complexation to metal extraction. The results suggest that both acid and sulfur(IV) ligand attack the reactive sites on the smelter slag surface to dissolve the metals. SO 2 (aq) was found to be more effective in leaching slag than H 2 SO 4 at the same pH. The effect of acid was more predominant at lower pH, while the ligand effect of SO 2 (aq) became stronger at higher pH. The leaching process was modeled to quantify the relative contribution of these two dissolution mechanisms using a new approach, which involves combining the shrinking-core model with an adsorption model. The model fit the leaching curves of Co, Ni, and Fe well and adequately described the experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".