Sustainable and Clean Process for Li<sub>2</sub>CO<sub>3</sub> and Co<sub>3</sub>O<sub>4</sub> Recovery from the Spent Lithium-Ion Battery via the Waste Graphite-Assisted Selective Sulfation Process
Bibliographic record
Abstract
Recovering valuable metals from spent lithium-ion batteries (LIBs) is crucial for environmental protection and resource sustainability. In this study, a novel accelerated selective sulfation roasting process is proposed for the recovery of valuable metals from a spent LiCoO 2 (LCO) cathode with the assistance of waste graphite. During the sulfation reaction, the waste graphite in spent LIBs promoted the selective extraction of lithium by accelerating the decomposition of CoSO 4 . Under the optimal conditions, i.e., a roasting temperature of 600 °C, a ferrous sulfate to LCO mass ratio of 1.4:1, and an added mass ratio of carbon to LCO of 20%, the leaching efficiencies of lithium and cobalt were approximately 99.29% and 0.17%, respectively. The sulfation mechanism of LCO was identified experimentally and with the help of density functional theory (DFT) calculations and followed two pathways. First, crystalline ferrous sulfate with a cubic crystal structure underwent desulfation, releasing the SO 2 . Next, generated SO 2 played a significant role in the gas–solid sulfation reaction with LCO. At an elevated temperature of 600 °C, the presence of carbon accelerated the selective sulfation reaction. DFT calculations further confirmed that carbon addition significantly reduced the energy barrier for the rate-controlling step in cobalt sulfate decomposition and thus accelerating the separation of lithium and cobalt. This study provided fundamental insights into the accelerated selective sulfation reaction, which contributed to the future development of methods for preferentially recovering lithium from the 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) | 0.001 | 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".