The CaO Enhanced Defluorination and Air-Jet Separation of Cathode-Active Material Coating for Direct Recycling Li-Ion Battery Electrodes
Bibliographic record
Abstract
With the rapid growth of the lithium-ion battery (LIBs) market, recycling and re-using end-of-life LIBs to reclaim the critical Li, Co, Ni, and Mn has become an urgent task. Presently, high temperature, strong acid, and alkali conditions are required to extract blended critical metals (CM) from the typical battery cathode. Hence, there is a need for more effective recycling processes for recycling blended Li, Co, Ni, and their direct regeneration for re-use in LIBs. The goal of the offered paper is the development of recycling technology for degraded battery cathode-active materials based on the thermal decomposition of polyvinylidene fluoride (PVDF) using calcination and air-jet stripping of active materials. The proposed air-jet erosion method of calcined cathode material stripping from Al foil allows for the flexible industry-applicable separation process, which is damage-free for both particles and substrate. The CaO calcination air-jet separation process and equipment can significantly improve the PVDF decomposition and the separation efficiency of the cathode materials. It is demonstrated that low-temperature CaO calcination at 350–450 °C associated with air-jet separation of active material is characterized by low environmental impact, high purity of the recycled material, and low cost as compared to pyro- and hydrometallurgical methods.
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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 | 0.000 | 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) | 0.001 | 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 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".