Selective separation of aromatics from alkanes with a green mixture <scp>MA</scp> / <scp>TeEG</scp>
Bibliographic record
Abstract
Abstract Liquid–liquid extraction separation of aromatics from alkanes facilitates their conversion into high‐value chemicals. Utilizing solvents composed only of C, H, and O elements can mitigate toxicity and environmental contamination from conventional solvents in liquid–liquid extraction separation of aromatics from alkanes. This study evaluated several C‐, H‐, and O‐based solvents mixed with maleic anhydride (MA), which tetraethylene glycol (TeEG) had the best effect. Through assessment of thermal stability and separation performance, the optimal extractant MA:TeEG (4:1) was identified. The selectivity for tetralin over n ‐dodecane using MA:TeEG (4:1) could reach 97.92 with a distribution coefficient of 0.1877, while the selectivity for toluene over n ‐heptane was 38.60 with a distribution coefficient of 0.2323, demonstrating its high selective separation efficiency. Multistage extraction experiments and reusability texts were further conducted. Quantum chemical calculations revealed that the selective extraction mechanism was due to variations in van der Waals forces between the solvent and target components.
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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".