Effective separation of aromatic hydrocarbons by pyridine‐based deep eutectic solvents
Bibliographic record
Abstract
Abstract Many promising qualities of deep‐eutectic solvents made them suitable solvents in separation process. In this work, the pyridine‐based deep eutectic solvents were designed and synthesized with N‐ethylpyridinium bromide and two HBDs (N‐formyl morpholine and levulinic acid). Two ternary systems, benzene + cyclohexane + DES and toluene + n‐heptane + DES, were studied by the liquid‐liquid extraction. The effect of different HBDs, extraction time, volume ratio of DES to system solution, and the initial concentration of aromatic were studied. The DES with N‐formyl morpholine showed better separation performance than that with levulinic acid. The liquid‐liquid extraction equilibrium could be obtained in 10 minutes. The volume ratio of DES to system solution was set as 1:1. Both DESs showed their best separation performance at low temperatures (20°C) and low aromatic concentration system. For the benzene + cyclohexane system, the distribution coefficient of benzene was 1.733 and the selectivity was 23.8 at 20°C. For the toluene + n‐heptane system, the distribution coefficient of toluene was 0.853 and the selectivity was 40.7. Tie‐lines for two ternary systems were obtained, and the Othmer‐Tobias correlation was used to check the reliability of the obtained liquid‐liquid extraction experimental data. The experimental LLE data were correlated using the NRTL model and the calculated data correlated significantly with the experimental data.
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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) | 0.000 | 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".