Recycling of mixed cathode lithium‐ion batteries for electric vehicles: Current status and future outlook
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it