Fluoride removal from NMC black mass leachates during Lithium-Ion battery recycling via aluminum sulfate precipitation
Bibliographic record
Abstract
This study presents a systematic approach for fluoride removal from pregnant leach solutions derived from NMC-type lithium-ion battery black mass using aluminum sulfate precipitation. The effects of pH, Al 2 (SO 4 ) 3 :F molar ratio, temperature, and reaction time are investigated to optimize fluoride removal while minimizing co-precipitation of valuable metals including lithium, nickel, cobalt, and manganese. At pH 5 and an Al 2 (SO 4 ) 3 :F ratio of 1.75, over 97% of fluoride was removed with less than 10% co-precipitation of valuable metals. An empirical model was developed to predict precipitation behavior, and model predictions showed good agreement with experimental results. Comparisons with OLI thermodynamic modeling revealed discrepancies in transition metal behavior, highlighting the value of the novel experimental data provided. These findings not only advance the development of efficient fluoride removal strategies but also offer a critical dataset that could support future refinement of thermodynamic databases used in hydrometallurgical modeling of lithium-ion battery recycling systems.
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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".