Recycling of NMC black mass from spent lithium-ion battery using supercritical fluid extraction
Bibliographic record
Abstract
The growing demand for lithium-ion battery recycling has intensified interest in selective and sustainable metal recovery techniques. In this study, we apply supercritical fluid extraction (SCFE) to real industrial black mass, containing mixed-metal oxides along with typical impurities such as conductive carbon, binder residues, and metallic fragments. Unlike previous SCFE research focused on pure cathode powders, this work demonstrates the feasibility and selectivity of the process under practical impurity conditions. Using supercritical CO 2 in combination with a tributyl phosphate–nitric acid adduct and hydrogen peroxide as a reducing agent, the process was optimized via full factorial design and response surface methodology. Under optimal conditions (65 °C, 20.7 MPa, 5 mL/g adduct-to-solid ratio in units of mL adduct per gram of black mass, 8 mL (per 8 g of feed) hydrogen peroxide), extraction efficiencies exceeded 90 % for Ni, Co, and Mn, while Li recovery reached 73 %. Characterization by XRD, SEM-EDX, Raman spectroscopy, and TC/TOC analysis confirmed metal removal and the structural persistence of carbonaceous material in the residue. Parametric tests highlighted the critical roles of the adduct and reducing agent in enabling efficient complexation and solubilization of transition metals. This work demonstrates that SCFE is a promising low-impact, chemically selective approach for lithium-ion battery recycling, capable of operating under mild conditions with reduced reagent consumption. It advances the feasibility of scalable, environmentally responsible recovery of critical materials from post-consumer batteries and sets the foundation for future integration with downstream purification or hybrid recycling technologies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".