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Record W2274217112

A STUDY OF ELECTROCHEMICAL INTERACTIONS BETWEEN GOLD AND ITS ASSOCIATED OXIDE MINERALS

2015· article· en· W2274217112 on OpenAlexaff
Ahmet Deniz Baş, Edward Ghali, Yeonuk Choi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHematiteMaghemiteMagnetiteIron oxideDissolutionMetallurgyGalvanic cellOxideElectrochemistryMaterials scienceCorrosionChemistryInorganic chemistryElectrode
DOInot available

Abstract

fetched live from OpenAlex

There is a lack of electrochemical studies of oxidized gold ores since the majority of previous studies were conducted with sulphidic gold ores. In this study, the influence of agitation and iron oxide minerals, i.e. magnetite, hematite, and maghemite on gold leaching was investigated by means of galvanic and passivation phenomena. Cyclic voltammetry studies of Roasted Gold Ore (RGO) in de-aerated electrolyte have shown one oxidation and one reduction reaction while three oxidation and one reduction peaks were observed with gold electrode. Galvanic coupling results by Zero Resistance Ammeter (ZRA) mode indicated that magnetite showed a negative effect on gold dissolution while maghemite and hematite showed a positive effect, relatively, as a result of higher galvanic corrosion rates. Generally, gold dissolution was found to increase in the initial 15 minutes. Increasing agitation speed from 100 to 400 rpm resulted in higher galvanic corrosion rates for RGO, magnetite, and maghemite electrodes, while 250 rpm was found to be optimum for hematite electrode. Passive products such as silver and iron-oxide were identified by XPS and SEM-EDS analysis, respectively.

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 score0.147
Threshold uncertainty score0.211

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 head0.054
GPT teacher head0.292
Teacher spread0.239 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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