An environmentally friendly system for high efficient silver recovery from anode slime
Bibliographic record
Abstract
Anode slime is an extremely valuable secondary resource for the recovery of silver and has attracted wide attentions. Effective metal recovery is usually constrained by either environmental hazards or high consumption of lixiviant for cyanide and non-cyanide leaching reagents. To tackle these issues in the process of silver recovery from anode slime, this research demonstrated a new leaching system with using an efficient oxidant which encapsulates cyanide into ferric complexes to ensure it not releasing into the environment while effective silver recovery was achieved. Comparing with the traditional tetra-amine copper oxidant, it was found that the dissolution rate of silver was significantly improved and leachate consumption was largely decreased. The effects of stirring speed, sodium thiosulfate concentration, oxidant concentration, solution pH and time on silver dissolution were investigated. When the novel system of iron-encapsulated cyanide was applied to extract silver from anode slime, the final conversion rates of silver was 97% after 60 minutes. This system has great potential in silver recovery from anode slime.
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 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 | 0.001 | 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 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".