Extraction and separation of potassium, zinc and manganese issued from spent alkaline batteries by a three‐unit hydrometallurgical process
Bibliographic record
Abstract
Abstract BACKGROUND Batteries play a vital role in meeting global energy needs. When their life cycle concludes, improperly discarded spent batteries can pose environmental risks primarily due to their metal content. In this sense, the recycling of metals contained in spent batteries could mean a huge advantage if they are extracted and purified using environmentally friendly processes. RESULTS In this study, the recovery of potassium (K), zinc (Zn) and manganese (Mn) from alkaline batteries was performed using a hydrometallurgical process consisting of neutral, acid and acid reductive leaching steps at room temperature and atmospheric pressure to extract K, Zn and Mn. In the neutral leaching step, 76.8 ± 3.4 (wt. %) of the K present in the spent batteries was extracted. Thus, in the acid leaching step, 90.9 ± 0.1 (wt. %) of the initial Zn and 36.7 ± 0.4 (wt. %) of the initial Mn was extracted using sulfuric acid (H2SO4) 2 M. In a subsequent acid reductive leaching step using H2SO4 2 M and oxygen peroxide (H2O2) 0.8 M as reducing agent, 8.7 ± 0.1 (wt. %) of the initial Zn and up to 49.4 ± 0.2 (wt. %) of the initial Mn were extracted. CONCLUSION The three‐unit process led to an overall extraction of 99.6 ± 0.3 (wt. %) of Zn and 86.1 ± 0.1 (wt. %) of Mn. Regarding the latter step, the extraction was not 100% because Mn complexes which are nearly insoluble were generated. This shows that extraction of valuable minerals from industrial residues is possible by hydrometallurgical processes. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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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".