Experimenting with Dimethyl Sulfoxide to Leach Gold from a Colombian Artisanal Gold Ore
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".