Effect of Surface Modification with Different Acids on the Functional Groups of AF 5 Catalyst and Its Catalytic Effect on the Atmospheric Leaching of Enargite
Bibliographic record
Abstract
Carbon-based catalysts can assist the oxidative leaching of sulfide minerals. Recently, we presented that AF 5 Lewatit® is among the catalysts with superior enargite oxidation capacity and capability to collect elemental sulfur on its surface. Herein, the effect of acid pre-treatment of the AF 5 catalyst was studied on the AF 5 surface, to further enhance the catalytic properties of AF 5. The AF 5 catalyst was pretreated by hydrochloric acid, nitric acid and sulfuric acid. The results showed that the acid treatment drastically changes the surface properties of AF 5. For instance, the concentration of quinone-like functional groups, which are ascribed to the catalytic properties of AF 5, is 45.4% in the sulfuric acid pre-treatment AF 5 and only 29.8% in the hydrochloric acid-treated AF 5. Based on the C 1s X-ray photoelectron spectroscopy (XPS) results the oxygenated carbon is 30.6% in the sulfuric acid-treated AF 5, 29.2% in the nitric acid-treated AF 5 and 28.3% in the hydrochloric acid-treated AF 5. The nitric acid pre-treated AF 5 resulted in the highest copper recovery during the oxidative enargite leaching process, recovering 98.8% of the copper. The sulfuric acid-treated AF 5 recovered 97.1% of the enargite copper into the leach solution. Among different leaching media and pre-treatment the lowest copper recovery was achieved with the HCl pre-treated AF 5 which was 88.6%. The pre-treatment of AF 5 with acids also had modified its elemental sulfur adsorption capacity, where the sulfur adsorption on AF 5 was increased from 30.9% for the HCl treated AF 5 to 51.1% for the sulfuric acid-treated AF 5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".