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Record W4312224623 · doi:10.3390/min13010052

Bioleaching of a Chalcocite-Dominant Copper Ore from Salta, Argentina, by Mesophilic and Thermophilic Microorganisms

2022· article· en· W4312224623 on OpenAlexfundno aff
Agustina Amar, Francisco L. Massello, Cristina Costa, Camila Castro, Edgardo Donati

Bibliographic record

VenueMinerals · 2022
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
FundersFirst Quantum Minerals
KeywordsChalcociteBioleachingMesophileCopperThermophileMicroorganismChemistryLeaching (pedology)MetallurgyEnvironmental scienceChalcopyriteMaterials scienceGeologyBacteria

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.200
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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