Effect of Ultrasound on the Oxidative Copper Leaching from Chalcopyrite in Acidic Ferric Sulfate Media
Bibliographic record
Abstract
The objective of this study is to compare the reaction kinetics of copper leaching from chalcopyrite in acidic ferric sulfate media with (UAL) and without (non-UAL) ultrasound assistance. Four leaching parameters were evaluated and optimized. The parameter with the strongest effect was temperature, followed by ultrasonic power, the solid-to-liquid ratio (S/L), and acid concentration. Copper recovery showed an increase with rising temperatures in both systems. Ultrasonic power had a positive effect on copper leaching, but no significant difference was found among various power amplitudes. Copper extraction increased with decreasing S/L. At 0.1% S/L, the UAL leaching rate was double the non-UAL leaching rate. In both systems, acid concentration had little effect on copper extraction. Under optimized conditions, 20% amplitude power, 1% S/L, 0.5 M acid, and 80 °C leaching temperature, copper extraction was 50.4% and 57.5% in the non-UAL and UAL treatments, respectively. Ultrasonic waves enhanced the leaching rate, shortened the reaction time, and reduced acid consumption. Analysis of the rate-controlling step using a shrinking core model showed that leaching occurs after diffusion through the product layer but also chemical controlled in both non-UAL and UAL systems. The leaching mechanism was confirmed by characterizing the chalcopyrite and leached residue with X-ray diffraction and scanning electron microscopy/energy dispersive X-ray spectroscopy.
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".