Recovery of Zn from Unsorted Spent Batteries Using Solvent Extraction and Electrodeposition
Bibliographic record
Abstract
This study focused on the selective recovery of zinc (Zn) from a leaching solution emerging from a sulfuric acid leaching process applied to unsorted spent batteries. Precipitation and solvent extraction were investigated. According to the results, solvent extraction using Cyanex 272 allowed for the selective removal of Zn from the solution containing high amounts of metals (∼19.4 g Zn/L, ∼23.4 g Mn/L, ∼3.27 g Cd/L, ∼3.19 g Ni/L, and ∼0.25 g Co/L). According to the results, the solvent extraction process was capable of recovering 97.6% of Zn from this leaching solution under the following conditions: two stages of extraction in the presence of an organic solution made of Cyanex 272 (30%, v/v) and tributylphosphate (TBP—2%, v/v) in kerosene, pH=2.2, organic/aqueous (O/A) ratio = 2/1, and T=50°C. The Zn present in the organic phase was then stripped using 0.4 M H2SO4 with an O/A ratio fixed at 2/1. This stripping step allowed for the recovery of 81.8% of the Zn initially present in the organic phase. Subsequently, 82.4% of the Zn stripped in the aqueous solution was then electrically deposited after 3 h at pH=2 with a current density fixed at 360 A/m2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".