Kinetic Study of Manganese Oxidative Precipitation Reaction by Using SO2/Air Gas Mixture
Bibliographic record
Abstract
Removing manganese from zinc electrolytes is necessary to pave the way for replacing lead-based anodes with mixed metal oxide (MMO) anodes. MMO anodes offer significantly lower overpotential towards oxygen evolution reactions, thus are attractive from an energy consumption viewpoint. Previous studies had shown that, thanks to the catalytic effect of cobalt, manganese can be removed successfully from the zinc purification solution through the oxidative precipitation method using a simulated roasting off-gas plant. This study focuses on understanding the primary mechanism behind manganese oxidation precipitation and investigating the influence of various operating parameters such as temperature, dissolved oxygen (DO), and solution potential on the reaction kinetics. The results revealed that the kinetics of the reaction was highly dependent on the temperature and catalyst activity rather than on the reactant concentration. Additives, with radical scavenging effects, were added to identify the radicals responsible for the oxidation of Mn. The manganese oxidation reaction was dramatically suppressed when methanol was added. However, in the presence of tert-butyl alcohol (TBA), a sensible reduction in manganese removal was not observed, suggesting sulfate radical as the predominant species for oxidizing manganese. The physical and chemical characteristics of the sediments were also presented.
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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 | 0.001 |
| 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".