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Record W4388424160 · doi:10.3390/met13111855

Experimenting with Dimethyl Sulfoxide to Leach Gold from a Colombian Artisanal Gold Ore

2023· article· en· W4388424160 on OpenAlexafffund
Pariya Torkaman, Akihiro Yoshimura, Leslie M. Lavkulich, Marcello M. Veiga

Bibliographic record

VenueMetals · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaEnvironmental Restoration and Conservation Agency
KeywordsGold cyanidationLixiviantGold extractionReagentLeaching (pedology)Gold miningCyanideChemistryGold oreSulfoxideEnvironmental chemistryEnvironmental scienceMetallurgyOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

The diverse uses of gold and its crucial role in the global economy are growing, particularly during cycles of economic crises. The broad use of cyanide by conventional gold-mining companies and mercury by artisanal miners poses environmental and health concerns for local communities. This article introduces an innovative gold-leaching process using a non-toxic organic reagent, dimethyl sulfoxide (DMSO), a water-free lixiviant that extracts gold from ores/concentrates in combination with copper halides. The results of laboratory experiments using dimethyl sulfoxide and a sample of high-grade gold ore from Colombia show that 96.5% of the gold was extracted in 2 h at room temperature. The typical cyanidation process using 5 g/L of CN− at pH 10.5 on the same ore sample obtained 97% gold extraction in 24 h at ambient temperature. The gold extracted using DMSO was precipitated by adding a mild acidic solution, and the reagent can be recycled via distillation and reused in repeating cycles. The results show that DMSO can be used as a promising agent for gold leaching, offering a straightforward, cost-effective, and eco-friendly procedure with minimal chemical waste.

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 categoriesInsufficient payload (model declined to judge)
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 score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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.

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
Published2023
Admission routes2
Has abstractyes

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