Bibliographic record
Abstract
The oxidative behavior of chalcopyrite and enargite in acidic ferric solutions was studied using surface characterization methods, leaching experiments and electrochemical techniques with massive electrodes and single fine particles. Results demonstrate that chalcopyrite oxidation displays classical active passive behavior, as often observed in passivating metals. Values predicted electrochemically on massive samples for the passivation potential Epp are in excellent agreement with leaching experiments in batch reactors. A transpassive regime was observed to appear after the passive regime and total dissolution of chalcopyrite was observed at potentials higher than 1.2 V vs. SCE. Passivated surfaces at low potentials between 300 and 550 mV showed non-stoichiometric chalcopyrite compounds and some isolated areas covered by sulfur. Passivated particles of chalcopyrite were reactivated with the addition of pyrite. At high potentials > 600 mV vs. SCE a dense sulfur layer was detected on particle surfaces and is assumed to be responsible for passivation at these potentials. Electrochemical studies of fine particles of enargite also showed active-passive behavior. The anodic active dissolution of enargite began at 300 mV and became passive at 700 mV vs. SCE. A compact sulfur layer on the surface of enargite particles was detected at potentials higher than 700 mV and caused passivation. Based on these electrochemical studies, enhancement of enargite leaching by addition of pyrite was proposed and validated. Leaching tests in batch reactors demonstrated that enargite can be dissolved effectively at atmospheric conditions producing elemental sulfur. Total extraction of copper was achieved within 24 h with a pyrite-to-enargite mass ratio of 4:1. Solid residues consisted entirely of porous elemental sulfur and all arsenic was found in the solution phase, predominantly as As(III). The implementation of this process at an industrial scale to leach chalcopyrite enargite concentrates will be significant, since there is no process operating at moderate temperatures and atmospheric conditions that is able to efficiently leach high-arsenic copper concentrates.
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".