Removal of Co(<scp>II</scp>) and various metal ions from the residue of the zinc plant through solvent extraction using both acid and neutral extractants
Bibliographic record
Abstract
Abstract The recovery of cobalt from secondary sources is a crucial issue in technology development, particularly for its numerous applications in various industries. Solvent extraction has proven an effective method for metallic ions separation from secondary sources. The main goal of this work was to study the separation of metals by solvent extraction technique from zinc plant leach waste. Initially, several extractants, including Cyanex 272, Cyanex 301, LIX 5640 H, Alamine 336, and tri‐n‐octylamine (TOA), were tested to treat the leach solution. It was discovered that the saponified extractant di‐(2‐ethylhexyl) phosphoric acid (D2EHPA) (10%) + tri‐n‐butyl phosphate (TBP) (10%) effectively eliminated interfering elements such as manganese, zinc, and iron, with extraction efficiencies of 95%, 98%, and 99.9%, respectively. To increase the concentration of cobalt in the purified solution, the cobalt recovery step was performed using various acids on the loaded organic phase. The best outcome was achieved using ammonium sulphate salt (1 M), which recovered about 50% of the cobalt present in the organic phase. The recovered cobalt was then subjected to the electrowinning process to produce cobalt metal of high purity. A cathodic current density of 100 A/m 2 was determined to be the optimal current for the electrolyte solution. Lastly, scanning electron microscope (SEM) and X‐ray fluorescence (XRF) analyses were carried out to examine the structure and purity of the resulting metallic cobalt, which was found to have a purity of 99.5%.
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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".