A greener method to recover critical metals from spent lithium-ion batteries (LIBs): Synergistic leaching without reducing agents
Bibliographic record
Abstract
Efficient recycling of critical metals from spent lithium-ion batteries is vital for clean energy and sustainable industry growth. Conventional methods often fail to manage large waste volumes, leading to hazardous gas emissions and dangerous materials. This study investigates innovative methods for recovering critical metals from spent LIBs using synergistic leaching. The first step optimized thermal treatment conditions (570 °C for 2 h in air) to remove binder materials while maintaining cathode material crystallinity, confirmed by X-ray diffraction (XRD) analysis. Next, response surface methodology (RSM), I-optimal, was used to examine the synergistic effects of sulfuric acid (SA) and organic acids (Org, citric and acetic acids) and their concentrations (SA: 0.5–2 M and Org: 0.1–2 M) on metal leaching for an eco-friendlier process. Results showed that adding citric acid to SA was more effective, especially at lower concentrations, than using acetic acid. The medium was tested to evaluate the impact of reductant addition. Remarkably, it was discovered that the optimized leaching mixture (1.25 M SA and 0.55 M citric acid) efficiently extracted metals without the need for any reductant like H2O2, highlighting its potential for a simpler and more eco-friendly recycling process. Further optimization identified the ideal solid-to-liquid ratio (62.5 g/L) to minimize acid use. Finally, RSM (D-optimal) was used to investigate the effects of time and temperature on leaching, achieving remarkable recovery rates of 99% ± 0.7 for Li, 98% ± 0.0 for Co, 90% ± 6.6 for Ni, and 92% ± 0.4 for Mn under optimized conditions at 189 min and 95 °C. Chemical cost analysis revealed this method is about 25% more cost-effective than conventional methods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".