Selective extraction of Al <sup>3+</sup> from the leaching solution of cathode materials of spent ternary lithium‐ion batteries by using <scp>D2EHPA</scp> ‐ <scp>TBP</scp> ‐kerosene
Bibliographic record
Abstract
Abstract An extraction system consisting of di‐2‐ethylhexyl phosphoric acid (D2EHPA), tributyl phosphate (TBP), and kerosene was developed for selective recovery of aluminium ions from the leaching solution of cathode materials of spent ternary lithium‐ion batteries. The proposed system could significantly separate nickel, cobalt, manganese, and lithium ions from aluminium ions in the leachate. A four‐stage cross‐flow extraction process using D2EHPA‐TBP‐kerosene with 40% saponification degree could recover 99.48% of Al 3+ at pH of 1.68 and O/A phase ratio of 1:1. After one‐stage washing and five‐stage stripping processes, a stripping solution containing nearly pure Al 3+ could be obtained. Then, the Al 2 (SO 4 ) 3 ‧ 17H 2 O product was recovered from the stripping solution by the cooling crystallization method with a yield of 92.63% and a purity of 99.32%. The extraction system showed very stable extraction ability during ten consecutive extraction–washing–stripping cycles. Finally, a cation exchange mechanism was explored by using Fourier transform infrared spectroscopy (FT‐IR) characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".