Selective Extraction of Critical Metals from Spent Lithium-Ion Batteries
Bibliographic record
Abstract
Selective and highly efficient extraction technologies for the recovery of critical metals including lithium, nickel, cobalt, and manganese from spent lithium-ion battery (LIB) cathode materials are essential in driving circularity. The tailored deep eutectic solvent (DES) choline chloride–formic acid (ChCl–FA) demonstrated a high selectivity and efficiency in extracting critical metals from mixed cathode materials (LiFePO 4:Li(NiCoMn) 1/3 O 2 mass ratio of 1:1) under mild conditions (80 °C, 120 min) with a solid–liquid mass ratio of 1:200. The leaching performance of critical metals could be further enhanced by mechanochemical processing because of particle size reduction, grain refinement, and internal energy storage. Furthermore, mechanochemical reactions effectively inhibited undesirable leaching of nontarget elements (iron and phosphorus), thus promoting the selectivity and leaching efficiency of critical metals. This was achieved through the preoxidation of Fe and the enhanced stability of iron phosphate framework, which significantly increased the separation factor of critical metals to nontarget elements from 56.9 to 1475. The proposed combination of ChCl–FA extraction and the mechanochemical reaction can achieve a highly selective extraction of critical metals from multisource spent LIBs under mild conditions.
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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) | 0.001 | 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".