Bibliographic record
Abstract
Tin is widely used in solder, tin plating and tin alloys. The current recovery rate of tin metal is low and insufficient with just over 300,000 tonnes annually. The grade of tin concentrates in traditional smelting methods needs to be at least 60%. Otherwise, iron, the chief impurity in tin concentrates can form tin-iron alloys and result in inefficient recovery of tin. Therefore, a hydrometallurgical technology to treat lower grade tin concentrates is proposed to solve the problem and close the demand gap for tin. The electrochemical reduction of chromium(III) solutions was conducted with a graphite felt cathode in acidic aqueous systems (chloride, sulfate and MSA). The parameters of acid concentration, current density, graphite felt thickness, graphite felt surface condition and graphite felt usage frequency were investigated. It was found that acid concentration has a significant influence on chromium(III) reduction in the sulfate and MSA system, while slight effect in the chloride system. In addition, the lifetime of the graphite felt in the sulfate and MSA system was shorter than that in the chloride system. These electrochemical differences may result from the pathway difference in electron transfer between the electrode and the chromium(III) ions. In general, chromium(III) ions in the chloride system showed the best electrochemical reduction activity. The chromium(II) ions synthesized from electrochemical reduction of chromium(III) ions were then used to effect the reduction of SnO₂ powder. The effect of temperature on the recovery test in the chloride, sulfate and MSA system was investigated. It was found that under the conditions of this thesis, the predominant recovery product of SnO₂ is Sn metal, rather than Sn(II). Generally, the recovery kinetics and total conversion were low in the sulfate and MSA system; however, the chloride system showed significantly better recovery results. This may be attributed to the catalysis effect of the chloride ions on the recovery process.
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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 | 0.000 | 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".