Bioleaching of a Chalcocite-Dominant Copper Ore from Salta, Argentina, by Mesophilic and Thermophilic Microorganisms
Bibliographic record
Abstract
The study and development of new sustainable mining methods to exploit low-grade ores and secondary metallic resources are essential to meet global demand and contribute to caring for the environment. Copper is one of the most widely used metals and chalcocite is the main secondary sulfide of this metal. Therefore, the study of copper recovery from chalcocite-dominant minerals could have a great impact on the industry. In this study, we assess at bench scale the feasibility of applying biohydrometallurgical processes to extract copper from chalcocite-rich minerals from Taca Taca, Argentina, using native mesophilic microorganisms (30 °C) and thermophiles (45, 65 °C). The indigenous mesophilic consortium was dominated by Acidithiobacillus ferrooxidans and could solubilize all the copper present in the systems (113 mg/L) within three weeks without any change in the pH of the solution. Notably, by increasing the temperature up to 45 and 65 °C, copper leaching was enhanced, completing the recovery in 7–14 days. The oxidizing microorganisms active in these conditions were Ferroplasma sp. and Acidianus copahuensis, respectively. An increase in the abiotic copper recovery was also observed as temperature rose; as well as a slight acidification of the solution. This study constitutes the first assessment for the bioleaching of Taca Taca ores.
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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 | 0.001 | 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 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".