Experimental Study on Recycling of Spent Lithium-Ion Battery Cathode Materials
Bibliographic record
Abstract
Spent lithium ion batteries (LIBs) are piling up from the electric-vehicle revolution and the increased demand in portable electronics. Currently, there is no environmentally friendly LIB recycle process really commercialized. This paper describes a series of experiments to advance the knowledge about recovery of metals from the spent battery cathode materials and to develop a novel environmentally friendlier closed-loop hydrometallurgical process. The leaching conditions are optimized by the bench scale experiments and various options for recovering critical LIB cathode metals are investigated. The semi-continuous locked-cycle campaigns document the dynamics of the recycled streams and yield much useful data. The behaviour of the leached cobalt is strongly affected by sulphate supersaturation and the location of sodium sulphate crystallization, but successful operation can be maintained when the sodium sulphate level is carefully controlled. These solution properties are the key factors when recycling spent cathode metals using systems based on sulphuric acid and sodium salts. Experimental results also show that the systems may reach supersaturation when circulating loads are incorporated, demonstrating the significance of sulphate levels in such systems. The closed-loop flowsheets are developed to recover metals, reduce discharges, and minimize environmental impact in the recycling of the spent cathode materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".