Upcycling of spent LiCoO<sub>2</sub> cathodes via nickel‐ and manganese‐doping
Bibliographic record
Abstract
Abstract Direct recycling has been regarded as one of the most promising approaches to dealing with the increasing amount of spent lithium‐ion batteries (LIBs). However, the current direct recycling method remains insufficient to regenerate outdated cathodes to meet current industry needs as it only aims at recovering the structure and composition of degraded cathodes. Herein, a nickel (Ni) and manganese (Mn) co‐doping strategy has been adopted to enhance LiCoO2 (LCO) cathode for next‐generation high‐performance LIBs through a conventional hydrothermal treatment combined with short annealing approach. Unlike direct recycling methods that make no changes to the chemical composition of cathodes, the unique upcycling process fabricates a series of cathodes doped with different contents of Ni and Mn. The regenerated LCO cathode with 5% doping delivers excellent electrochemical performance with a discharge capacity of 160.23 mAh g−1 at 1.0 C and capacity retention of 91.2% after 100 cycles, considerably surpassing those of the pristine one (124.05 mAh g−1 and 89.05%). All results indicate the feasibility of such Ni–Mn co‐doping‐enabled upcycling on regenerating LCO cathodes.
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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".