An environmentally friendly method for efficient atmospheric oxidation of pyrrhotite in arsenopyrite/pyrite calcine
Bibliographic record
Abstract
Pyrite and arsenopyrite are the most common hosts for invisible gold, but pyrite and arsenic are refractory during conventional sulfide oxidation, which significantly challenges subsequent gold extraction. One option is high-temperature pretreatment of arsenical materials to sequester > 90% of the arsenic as a gas, then convert it to a stable form. This process produces a calcine similar in composition to pyrrhotite (Fe1-xS) but with higher porosity. In this study, the calcine product is oxidized with an efficient, cost-effective atmospheric process using acidic and near-neutral solutions. A sulfur mass balance analysis method based on iron sulfide thermal transformation in nitrogen atmosphere was developed to quantify the oxidation efficiency of pyrrhotite leaching. The optimization confirmed that > 90% of the calcine was oxidized by Fe3+ (5 and 10 g/L) and O2 (0.5 L/min) at pH 1 after 48 h and at 95 °C even without ultrafine grinding. Elemental sulfur was the main oxidation product when the oxidation pH was 1,2. This study provides the foundation for the development of a low-cost and environmentally friendly process option for pretreatment of arsenical sulfide refractory gold materials.
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) | 0.000 | 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".