Selective zinc recovery from spent alkaline batteries via multistage leaching with ammonium salts
Bibliographic record
Abstract
The recycling of metals from spent alkaline batteries is essential for their proper management and for promoting sustainable battery consumption. Hydrometallurgical recycling techniques, such as leaching, are becoming important in batteries recycling. In this study, Zn has been selectively recovered from the black mass (BM) of spent alkaline batteries via chelating leaching using ammonium salts as chelating agents in single and multistage leaching units. The effect of leaching agent concentration, temperature, solid/liquid (S/L) ratio, a neutral leaching pretreatment and addition of ammonium hydroxide (NH 4 OH) to the leaching solution on the selective Zn extraction was studied. Results of single-stage leaching revealed a maximum Zn extraction efficiency of 69.3 ± 0.4 wt % using a 2M ammonium carbonate ((NH 4 ) 2 CO 3 ) solution at 25 °C and S/L ratio of 1/10 (g of BM/mL of solution). The addition of NH 4 OH 1M increased Zn extraction to 79.0 ± 1.9 wt %. These single leaching conditions were used to test three multistage leaching systems: solid-flowing in series, liquid-flowing in series and solid-liquid countercurrent. The recovery efficiency was maintained and sometimes it was improved in multistep configurations, reaching a maximum recovery efficiency of nearly 90 wt%. Additionally, cumulative zinc extraction across the multistage leaching setups was as follows: 145.6 g Zn/kg BM in the 3-unit-solid-flowing in series, 433.5 g Zn/kg BM in the 4-unit-liquid-flowing in series, and 132.46 g Zn/kg BM in two-unit countercurrent leaching. These concentrations were obtained using a raw BM containing 240.9 g Zn/kg BM. These results show that zinc can be selectively extracted from matrices containing other metals, allowing the development of efficient and cost-effective methods for recycling resources from spent batteries.
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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) | 0.000 | 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".