Opportunities to Improve Recycling of Automotive Lithium Ion Batteries
Bibliographic record
Abstract
A high recovery of lithium from recycled lithium ion batteries (LIBs) is essential to ensure the growth and sustainability of the electrical vehicle market. Without recycling, lithium demand is predicted to outstrip supply in 2023. Current industrial processes are focused on recovering cobalt and other valuable metals because, given lithium's current low price, it is economically unfavorable to recover it. As part of our efforts to create a process where the recovery of lithium is economically viable we have analyzed the current industrial processes. We have determined that, when applied to recycling automotive LIBs, they are needlessly energy intensive and complicated. In these processes whole LIBs are incinerated, cryogenically cooled, or shredded under an inert atmosphere in order to make their cells safe to open. Instead of such extreme measures, LIBs can be disassembled by automated processes, which recovers valuable electronics for reuse, their cells can be discharged, which recovers residual energy, and then can be opened safely in air.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".