Recent Developments in the Photocatalytic Treatment of Cyanide Wastewater: An Approach to Remediation and Recovery of Metals
Bibliographic record
Abstract
For gold extraction, the most used extraction technique is the Merrill-Crow process, which uses lixiviants as sodium or potassium cyanide for gold leaching at alkaline conditions. The cyanide ion has an affinity not only for gold and silver, but for other metals in the ores, such as Al, Fe, Cu, Ni, Zn, and other toxic metals like Hg, As, Cr, Co, Pb, Sn, and Mn. After the extraction stage, the resulting wastewater is concentrated at alkaline conditions with concentrations up to 1000 ppm of metals. Photocatalysis is an advanced oxidation process (AOP) able to generate a photoreaction in the solid surface of a semiconductor activated by light. Although it is well known that photocatalytic processes can remove metals in solution, there are no compilations about the researches on photocatalytic removal of metals in wastewater with cyanide. Hence, this review comprises the existing applications of photocatalytic processes to remove metal and in some cases recover cyanide from recalcitrant wastewater from gold extraction. The use of this process, in general, requires the addition of several scavengers in order to force the mechanism to a pathway where the electrons can be transferred to the metal-cyanide matrices, or elsewhere the entire metallic cyanocomplex can be degraded by an oxidative pathway.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".