Biogenic Production of Thiosulfate from Organic and Inorganic Sulfur Substrates for Application to Gold Leaching
Bibliographic record
Abstract
Gold mining and processing is an activity with large environmental impact due to the low concentration of gold in ore deposits and chemical resistance to most chemicals. Over 75% of gold is leached from ores using cyanide, however less toxic lixiviants have been proposed in the literature. Thiosulfate is one of these alternative reagents, but high reagent consumption has slowed acceptance in mining operations. Reducing the cost and impact of thiosulfate production is a way to reduce the cost of reagent consumption during leaching. The objective of this study was to evaluate the feasibility of leaching gold from ore with biogenic thiosulfate. Biogenic thiosulfate was produced using a marine methylotroph bacterium from three substrates: sodium sulfide, elemental sulfur, and dimethyl sulfide for application in bioleaching. The different substrates were evaluated to determine conversion efficiency from the sulfur source to biogenic thiosulfate and verified by titration and ion chromatography. Optimal conditions for conversion to thiosulfate were determined to be in the range of pH = 7–8, 25–30 °C, with sodium sulfide as a substrate in a sealed system to prevent sulfide from escaping as hydrogen sulfide gas. An oxide gold ore with a grade of 4.02 g/t was selected as a gold source for leaching experiments. The leaching of gold using the biogenic thiosulfate was compared with chemical thiosulfate solutions under experimental conditions of pH = 9.5, 50 mg/L copper, 500 RPM mixing, and 0.1 L/min air. The efficiency of gold bioleaching was measured using flame atomic absorption spectroscopy and fire assay. Gold extraction efficiencies ranging from 20–60% were achieved using the biogenic thiosulfate, and 27–77% with sodium thiosulfate solutions, respectively. It was concluded that the sodium sulfide substrate was best for producing higher biogenic thiosulfate concentrations and leaching efficiency.
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 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) | 0.001 | 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".