Performance and mechanism of the separation of <scp>C8</scp> α‐olefin from <scp>F‐T</scp> synthesis products using novel <scp>Ag‐DES</scp>
Bibliographic record
Abstract
Abstract As an attractive alternative technology for the separation of long chain olefin and paraffin, a novel silver‐based deep eutectic solvent (Ag‐DES) was prepared and utilized for 1‐octene/n‐octane separations. Comprehensive reactive extraction separation experiments were performed to highlight the Ag‐DES concentration and operating temperature discriminations using compounds with different ratio of 1‐octene/n‐octane. The novel Ag‐DES showed optimal separation performance regarding 1‐octene/n‐octane and possessed the highest levels separation selectivity in the range 3.57–16.11 with excellent circulation stability in our best knowledge. Furthermore, FT‐Raman measurements and quantum chemistry calculation were performed to elucidate the interaction mechanism of Ag‐DES in the separation of 1‐octene and n‐octane, which revealed that both chemical complexation and strong physical attraction existed in the complex of Ag‐DES and 1‐octene rather than n‐octane. A practical process was proposed for the separation of olefin and paraffin, which indicates that an advanced separation technology could largely reduce the energy consumption. This study lends important insight for the development of Ag‐DES reactive extraction separation process for the energy‐efficient long chain α‐olefins purification from F‐T synthesis products.
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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".