Reviving Riches: Unleashing Critical Minerals from Copper Smelter Slag Through Hybrid Bioleaching Approach
Bibliographic record
Abstract
Due to the emission of hazardous chemicals and heat, the traditional smelting method used to extract critical minerals from ore and mine slag/tailings is considered bad for the environment. An environmentally friendly procedure that can stabilize sulfur emissions from mine waste without endangering the environment is bioleaching. In the present study, sequential oxidative (Oxi) and reductive (Red) bioleaching of acid-pretreated copper smelter slag using iron-oxidizing/reducing Acidithiobacillus ferrooxidans was applied to investigate critical minerals’ recovery for the dissolution of copper smelter slag. In this batch flask experiment, up to 55% Cu was recovered on day 11 during the Oxi stage, which increased to 80% during the Red stage on day 20. A sequential oxidative and reductive bioleaching of an acid-pretreated copper smelter slag at pH (1.8) and 30 °C positively affects the extraction of Cu (80%), Zn (77.1%), and Al (65.3%). In contrast to the aerobic bioleaching experiment, the reduction of Fe3+ iron under anaerobic conditions resulted in a more significant release of Fe2+ and sulfate, limiting the development of jarosite, surface passivation, and the subsequent loss of metal recovery due to co-precipitation with Fe3+. Overall, the Oxi-Red bioleaching process combined with acid pretreatment showed promising results toward creating a method for recovering valuable metals from metallurgical waste that is economical and environmentally beneficial.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".