A green approach for cohesive recycling and regeneration of electrode active materials from spent lithium‐ion batteries
Bibliographic record
Abstract
Abstract The implementation of the green energy transition by reducing reliance on fossil fuels has fueled the burgeoning demand for lithium‐ion batteries in grid‐level energy storage systems and electric vehicles. The growth of portable electronic devices has also contributed to this exponential demand, creating both logistical and environmental challenges in the supply of raw materials such as lithium and the management of end‐of‐life batteries. Current recycling methods for spent batteries are both energy‐intensive and inefficient. To address these issues, a green approach using organic acid mixtures has been proposed to reclaim lithium from spent cathodes and recover and purify graphite from spent anodes, while also regenerating its structure. The effectiveness of this method is demonstrated through the use of organic acid mixtures to leach and reclaim lithium from NCM 622 batteries. On the anode side, a curing–leaching strategy using organic acids is employed to purify spent graphite, which is subsequently calcined to enhance its interlayer structure conducive to better intercalation of Li+ and improve electrochemical performance. Additionally, recovered graphite is tailored with carbon using water bath carbonization to repair structural defects caused by lithium intercalation and improve electrochemical performance while augmenting the regenerated graphite's quality, equipping it to be reused in batteries or upcycled applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".