Sustainable Recovery of Critical Metals from Spent Lithium-Ion Batteries Using Deep Eutectic Solvents
Bibliographic record
Abstract
The surging demand for lithium-ion batteries (LIBs) has intensified the need for sustainable recovery of critical metals such as lithium, manganese, cobalt, and nickel from spent cathodes. While conventional hydrometallurgical and pyrometallurgical methods are widely used, they involve high energy consumption, hazardous waste generation, and complex processing steps, underscoring the urgency of developing eco-friendly alternatives. This study presents a novel, water-enhanced deep eutectic solvent (DES) system composed of choline chloride and D-glucose for the efficient leaching of valuable metals from spent LiMn-based battery cathodes. The DES was synthesized under mild conditions and applied to dissolve cathode powder, with leaching performance optimized by varying temperature and duration. Under optimal conditions (100 °C, 24 h), exceptional recovery efficiencies were achieved: 98.9% for lithium, 98.4% for manganese, and 71.7% for nickel. Material characterization using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and inductively coupled plasma mass spectrometer (ICP-MS) confirm effective phase dissolution and metal release. Although this DES system requires relatively higher temperature and longer reaction time compared to traditional acid leaching, it offers clear advantages in terms of non-toxicity, biodegradability, and elimination of strong oxidizing agents. These results demonstrate the potential of water-enhanced choline chloride–glucose DES as a green alternative for future development in sustainable battery recycling, supporting circular economy objectives.
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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".