Metal recovery from spent lithium-ion batteries via two-step bioleaching using adapted chemolithotrophs from an acidic mine pit lake
Bibliographic record
Abstract
The demand for lithium-ion batteries (LIBs) has dramatically increased in recent years due to their application in various electronic devices and electric vehicles (EVs). Great amount of LIB waste is generated, most of which ends up in landfills. LIB wastes contain substantial amounts of critical metals (such as Li, Co, Ni, Mn, and Cu) and can therefore serve as valuable secondary sources of these metals. Metal recovery from the black mass (shredded spent LIBs) can be achieved via bioleaching, a microbiology-based technology that is considered to be environmentally friendly, due to its lower costs and energy consumption compared to conventional pyrometallurgy or hydrometallurgy. However, the growth and metabolism of bioleaching microorganisms can be inhibited by dissolved metals. In this study, the indigenous acidophilic chemolithotrophs in a sediment from a highly acidic and metal-contaminated mine pit lake were enriched in a selective medium containing iron, sulfur, or both electron donors. The enriched culture with the highest growth and oxidation rate and the lowest microbial diversity (dominated by Acidithiobacillus and Alicyclobacillus spp. utilizing both electron donors) was then gradually adapted to increasing concentrations of Li + , Co 2+ , Ni 2+ , Mn 2+ , and Cu 2+ . Finally, up to 100% recovery rates of Li, Co, Ni, Mn, and Al were achieved via two-step bioleaching using the adapted culture, resulting in more effective metal extraction compared to bioleaching with a non-adapted culture and abiotic control.
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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 | 0.001 | 0.001 |
| 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".