Recycling of mixed cathode lithium‐ion batteries for electric vehicles: Current status and future outlook
Bibliographic record
Abstract
Abstract Worldwide trends in mobile electrification, largely driven by the popularity of electric vehicles (EVs) will skyrocket demands for lithium‐ion battery (LIB) production. As such, up to four million metric tons of LIB waste from EV battery packs could be generated from 2015 to 2040. LIB recycling directly addresses concerns over long‐term economic strains due to the uneven geographic distribution of resources (especially for Co and Li) and environmental issues associated with both landfilling and raw material extraction. However, LIB recycling infrastructure has not been widely adopted, and current facilities are mostly focused on Co recovery for economic gains. This incentive will decline due to shifting market trends from LiCoO 2 toward cobalt‐deficient and mixed‐metal cathodes (eg, LiNi 1/3 Mn 1/3 Co 1/3 O 2 ). Thus, this review covers recycling strategies to recover metals in mixed‐metal LIB cathodes and comingled scrap comprising different chemistries. As such, hydrometallurgical processes can meet this criterion, while also requiring a low environmental footprint and energy consumption compared to pyrometallurgy. Following pretreatment to separate the cathode from other battery components, the active material is dissolved entirely by reductive acid leaching. A complex leachate is generated, comprising cathode metals (Li + , Ni 2+ , Mn 2+ , and Co 2+ ) and impurities (Fe 3+ , Al 3+ , and Cu 2+ ) from the current collectors and battery casing, which can be separated and purified using a series of selective precipitation and/or solvent extraction steps. Alternatively, the cathode can be resynthesized directly from the leachate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".