Efficient extraction and theoretical insights for separating <i>o</i> ‐, <i>m</i> ‐, and <i>p</i> ‐cresol from model coal tar by an ionic liquid [ <scp>Emim</scp> ][ <scp>DCA</scp> ]
Bibliographic record
Abstract
Abstract Cresols are valuable phenolic chemicals which are mainly separated from coal tar. The separation of cresols using sustainable technology is of importance for comprehensive utilization of coal tar. In this work, for extracting o ‐, m ‐, and p ‐cresol from the model coal tar, an ionic liquid 1‐ethyl‐3‐methylimidazolium dicyanamide ([Emim][DCA]) was prepared. The influence of ionic liquid dosage, extraction time, and temperature on separating o ‐, m ‐, and p ‐cresol was explored. The extraction efficiency was determined from the experimental results to assess the extraction ability of [Emim][DCA]. With the optimal conditions, o ‐, m ‐, and p ‐cresol were extracted efficiently from the model coal tar and the extraction efficiencies of o ‐cresol, m ‐cresol, and p ‐cresol were up to 99.50%, 99.65%, 98.38%, respectively. In the meantime, the extraction mechanism was determined by calculation of σ ‐profile, interaction energy, and electron density. The results showed that [Emim][DCA] and cresols formed the complexes by hydrogen bond. The formation of hydrogen bond was verified by the FTIR spectra, which is helpful for designing the related ionic liquids for recovering the phenolic compounds from coal tar. In addition, the recyclability of the [Emim][DCA] was explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".