Metal Reclamation from Spent Lithium-Ion Battery Cathode Materials: Directional Conversion of Metals Based on Hydrogen Reduction
Bibliographic record
Abstract
The vast amount of spent lithium-ion batteries (LIBs), after exhausting their useful life, necessitates comprehensive recycling for metal reclamation. This paper proposed using hydrogen as a green reductant to reduce the cathode materials of spent LIBs, followed by wet magnetic separation. The inspiration behind this is to realize pollution-free and highly efficient separation of high-value metals from LIB cathode materials by utilizing hydrogen reduction to attain directional conversion of metals based on water solubility and magnetism differences. The effect of two major variables, that is, reduction temperature and time, on the leaching efficiency of Li and recovery of Ni, Co, and Mn, are investigated. The experimental results showed that the nickel–cobalt–manganese LIB cathode was primarily transformed into water-soluble Li 2 O, magnetic Ni–Co alloy, and nonmagnetic manganese oxides after hydrogen reduction under optimum conditions of 800 °C for 90 min. The reduction products underwent water-leaching to recover Li with a recovery of 96.8 wt %. Ni and Co are further separated magnetically from manganese oxides with recovery rates of 99.8 wt % Ni, 99.4 wt % Co into the magnetic fraction, and 90.3 wt % Mn into the nonmagnetic fraction.
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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".