Recycling Lithium-Ion Battery: Mechanical Separation of Mixed Cathode Active Materials
Bibliographic record
Abstract
Abstract Lithium-ion batteries (LIBs) have driven the industry of rechargeable batteries in recent years due to their advantages such as high energy and power density and relatively long lifespan. Nevertheless, the dispose of spent LIBs has harmful impacts on the environment which needs to be addressed by recycling LIBs. However, none of the currently developed recycling processes is economical. The physical recycling process of LIBs may be economical if the cathode active materials can be separated, regenerated, and reused to make new LIBs. However, the first barrier for regeneration and reusing is the separation of different types of spent cathode active materials in the filter cake that are mixed with each other and come in the form of very fine powders with various sizes (< 30 μm) from the physical recycling process. The aim of this study is to separate the mixture of cathode active materials by adopting Stokes’ law. The focus will be only on mechanical separation with no thermal or chemical separation methods. For the validation, an experiment was designed and successfully performed where different types of spent cathode materials (e.g., LiCoO2, LiFePO4, and LiMn2O4) were separated from the spent anode materials (e.g., graphite) with high efficiency and reasonable time.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".