Gold Cementation from Ammonium Thiosulfate Solution by Zinc, Copper and Aluminium Powders
Bibliographic record
Abstract
Gold cementation test was conducted without de-aeration by using zinc, copper and aluminium powders from an ammonium thiosulfate solution contained 8 mg/l Au. The amount of metal powder was varied in the range of 30–450 Metal/Gold mass ratio. The solution composition was 1–5 mol/l NH4OH, 0.01–0.05 mol/l CuSO4∗5H2O, 0.2–0.4 mol/l (NH4)2S2O3 and pH 9.5–10.5. The results indicated that the gold was effectively recovered from a solution of lower ammonia and copper concentrations and higher thiosulfate concentration. The optimum reagent composition for the gold cementation from the ammonium thiosulfate solution was founded to be 1 mol/l NH4OH, 0.01 mol/l CuSO4∗5H2O and 0.4 mol/l (NH4)2S2O3 at pH 9.5. 100% of gold was recovered by zinc and aluminium powders at a Metal/Gold mass ratio of 30. Copper powder recovered 93% of gold at a Metal/Gold mass ratio of 50. Zinc might re-generate thiosulfate concentration and precipitate most of copper in the solution. Aluminium precipitation might recover gold with less amount of copper deposition and some thiosulfate reduction. Copper precipitation reduced a small amount of thiosulfate concentration and greatly increased copper concentration. Ammonia concentration stayed constant during cementation process.
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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) | 0.001 | 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) | 0.001 | 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".