The Recycling of Plastics and Current Collector Foils from End-of-Life NMC-LCO Type Electric Vehicle Lithium-Ion Batteries Using Selective Froth Flotation
Bibliographic record
Abstract
The recent increase in end-of-life (EoL) lithium-ion batteries (LiBs) has become a significant concern worldwide. Most studies in the literature have primarily focused on recovering cathode active metals from black mass (BM), whereas the separation of anode–cathode foils, plastics, and casing metals which are the essential components of LiBs has received relatively little attention. To reduce costs and maximize the recovery of valuable metals in subsequent hydrometallurgical or pyrometallurgical processes, EoL LiBs require appropriate pre-treatment. This study aims to scrape off the BM adhering to the electrode foils resulting from gradual crushing and subsequently separate the plastics and copper (Cu) from other metals through a two-step selective flotation process. The results demonstrated that plastics, due to their natural hydrophobicity, could be effectively removed using a frother, achieving more than 95% recovery with less than 5% metallic contamination. Following plastic flotation, Cu particles were floated in the presence of 3418A, yielding a Cu concentrate containing 65.13% Cu with a recovery rate of 96.4%. Additionally, the aluminum (Al) content in the non-floating material, remaining in the cell, increased to approximately 77%.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".