Mechanism and Kinetics for Copper Leaching by Complexing with Lysine Bearing Two Amino Groups
Bibliographic record
Abstract
In order to overcome the environmental problems associated with ammonia volatilization during the ammonia leaching process, the leaching effects of copper by amino acids with various numbers and substitution positions of amino functional groups were systematically studied. The results showed that lysine had the best leaching efficiency for copper. When the concentration of lysine was 0.2 mol/L, the leaching temperature was 20 °C, the molar ratio of lysine to copper was 3.88, the stirring speed was 250 rpm, the pH was 10, and the leaching time was 14 h, the leaching rate of copper reached 99%. The leaching results of copper containing smelting slag showed that lysine has an excellent effect on selective leaching of copper. The leaching kinetic results indicated that the rate-limiting step of the leaching process is controlled by the interfacial chemical reaction, with an apparent activation energy of 64.8 kJ/mol. Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS) analyses confirmed that COO – and –NH groups in lysine can form complexes with copper ions. Compared to conventional methods, this approach not only achieves environmentally benign disposal of heavy metal pollutants but also provides a novel strategy for the green recovery of valuable metals from secondary resources.
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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) | 0.001 | 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".