Supercritical Fluid Extraction of Lithium-Ion Battery Materials: Predictive Modeling and Mechanistic Insights Using COSMO–DFT Framework
Bibliographic record
Abstract
Supercritical fluid extraction (SCFE) has emerged as a promising strategy for recovering critical battery metals from end-of-life lithium-ion batteries (LIBs), offering a sustainable alternative to conventional hydrometallurgical approaches. In this study, we investigate the SCFE of lithium (Li), nickel (Ni), cobalt (Co), manganese (Mn), aluminum (Al), and copper (Cu) from NMC black mass. The process utilizes a TBP–HNO 3 complex with supercritical CO 2 and includes the addition of hydrogen peroxide (H 2 O 2 ) as a reducing agent. H 2 O 2 facilitates the conversion of high-valent metal ions (e.g., Co 3+, Ni 3+ ) to divalent forms, enhancing their solubility and enabling the formation of extractable metal–ligand complexes in the nonpolar sc-CO 2 phase. To elucidate the extraction mechanism and predict solubility, sc-CO 2 /water partition coefficients of metal–nitrate–TBP complexes were calculated using the COSMO-vac model, with molecular structures optimized via density functional theory (DFT). The calculated partition coefficients align closely with experimental extraction trends, confirming the model’s predictive capability. Additionally, the roles of oxidation state, system pressure, and water coordination in influencing extraction efficiency were systematically examined. This work demonstrates the utility of COSMO-based modeling in guiding SCFE process design and highlights the potential of SCFE for sustainable critical metal recovery.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".