Recovery of valuable metals from cathode materials of spent ternary lithium‐ion battery using natural product as reducing reagent
Bibliographic record
Abstract
Abstract In recent years, ternary lithium‐ion batteries (LIBs) have been vigorously promoted in the field of new energy vehicles for their excellent overall performance, and the rapid growth of battery production and sales has brought about a proliferation of spent LIBs. The recycling of cathode materials of spent ternary LIBs, which are rich in many valuable metal elements, will bring multiple environmental and economic benefits. In this paper, a systematic study of the key influencing factors in the wet leaching and recycling process was carried out. A natural product tea polyphenol was innovatively used as the reducing reagent, and a sulphuric acid–tea polyphenol leaching system was constructed for the reductive acid leaching of cathode materials of spent ternary LIBs. Under the optimum leaching conditions, the leaching efficiencies of lithium, nickel, cobalt, and manganese were 99.54%, 98.88%, 99.37%, and 98.45%, respectively. After leaching, a stepwise precipitation method was used to separate and recover the valuable metal ions from the leachate. In summary, an innovatively complete recycling process route for cathode materials of spent ternary LIBs was constructed, which provides a solid theoretical basis and technical support for the efficient and clean recycling of spent LIBs.
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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) | 0.000 | 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".