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.
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 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 | 0.000 | 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 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".