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Record W4248435914 · doi:10.17758/eares10.eap1120215

Chalcopyrite Dissolution in Acidified ferric sulfate: a Thermodynamic Study of Intermediate Phases

2020· article· en· W4248435914 on OpenAlexaff
F.B. Waanders, Martin Mkandawire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsCape Breton University
FundersNational Research Foundation
KeywordsChalcopyriteDissolutionSulfateFerricChemistryInorganic chemistryMetallurgyMaterials sciencePhysical chemistryCopper

Abstract

fetched live from OpenAlex

The leaching of chalcopyrite (CuFeS 2 ) is characterized by slow Cu dissolution rate and poor recoveries, mainly at atmospheric pressure and low temperature. It is believed that, the parabolic rate is caused by the solid-state transformation that takes place during mineral dissolution which leads to the formation of a dissolution barrier that prevents further Cu removal from CuFeS 2 . In this study, the dissolution of copper from CuFeS 2 mineral was investigated in acidified ferric sulfate at atmospheric pressure. The results revealed that only 22% Cu were recovered after 5 hours at 50 o C at a solution pH of 1.8. It was observed that, the dissolution of Cu was accompanied by intermediate phases formation which seem to have a retarding effect on the direct oxidation of CuFeS 2 . The transient metastable phases were Copper sulphides rich minerals including bornite (Cu 5 FeS 4 ), chalcocite (Cu 2 S) and covellite (CuS). Thermodynamic predictions revealed that Cu 5 FeS 4 and Cu 2 S were the soluble intermediates and were characterized by a slow dissolution rate. Whereas, CuS has been identified as a refractory intermediate inhibiting Cu dissolution. The dissolution phase diagram was obtained and further discussed with regard to the intermediate phase formation, evolution, transformation and dissolution.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.293

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)0.0000.000

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 head0.029
GPT teacher head0.253
Teacher spread0.224 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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