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Record W3198091386 · doi:10.3390/min11090963

Iron-Speciation Control of Chalcopyrite Dissolution from a Carbonatite Derived Concentrate with Acidic Ferric Sulphate Media

2021· article· en· W3198091386 on OpenAlexafffund
Kolela J. Nyembwe, Elvis Fosso‐Kankeu, F.B. Waanders, Martin Mkandawire

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsCape Breton University
FundersResearch Nova ScotiaNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities AgencyNova Scotia Research Innovation Trust
KeywordsDissolutionChalcopyriteFerricChemistryCarbonatitePhase (matter)Inorganic chemistryCopperGeologyGeochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The mechanisms involved in the dissolution of chalcopyrite from a carbonatite concentrate in a ferric sulphate solution at pH 1.0, 1.5 and 1.8, and temperatures 25 °C and 50 °C were investigated. Contrary to expectations and thermodynamic predictions according to which low pH would favour high Cu dissolution, the opposite was observed. The dissolution was also highly correlated to the temperature. CuFeS2 phase dissolution produced intermediate Cu rich phases: CuS, Cu2S and Cu5FeS4, which appeared to envelop CuFeS2. Thermodynamic prediction revealed CuS to be refractory and could hinder dissolution. CuFeS2 phase solid-state dissolution process was further discussed. Free Fe3+ and its complexes (Fe(HSO4)2+, Fe(SO4)2– and FeSO4+ were responsible for Cu dissolution, which increased with increasing pH and temperature. The dissolution improved at pH 1.8 rather than 1.0 due to the increase of (Fe(HSO4)2+, Fe(SO4)2– and FeSO4+, which were also the predominating species at a higher temperature. The fast and linear first dissolution stage was attributed to the combined effect of Fe3+ and its complex (Fe(HSO4)2+, while Fe(SO4)2– was the main species for the second Cu dissolution stage characterised by a slow rate.

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 score0.248
Threshold uncertainty score0.455

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 head0.011
GPT teacher head0.202
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

Citations2
Published2021
Admission routes2
Has abstractyes

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