Polyethylenimine-Assisted Extraction of α-Tocopherol from Tocopherol Homologues and CO<sub>2</sub>-Triggered Fast Recovery of the Extractant
Bibliographic record
Abstract
The separation of natural homologues provides a great challenge due to high similarities of their structures and properties. In this work, the use of the polymeric extractant polyethylenimine (PEI) for the separation of α-tocopherol from the tocopherol homologues was investigated. Various PEI–cosolvent solutions were used to extract tocopherols from their hexane solutions. The effects of PEI molecular weight, cosolvent type, and PEI–cosolvent composition were systematically studied. High distribution and selectivity coefficients of tocopherols were obtained with PEI–acrylonitrile (ACN) as the extractant. A clear advantage of PEI extractant is its stimuli-responsive property triggered by CO 2 . PEI–extracted tocopherols could be replaced and released from PEI chains with CO 2 bubbling, with PEI–CO 2 precipitated out from the extract phase, which greatly facilitates the back extraction of tocopherols and the recovery of PEI for reuse. With CO 2 bubbling, it took only three back extractions to recover over 90% tocopherols, compared with 12 in theory without CO 2 treatment. Precipitated PEI–CO 2 could be redissolved in cosolvent for reuse by N 2 bubbling and heating. The distribution coefficients decreased by approximately 10% after being recycled three times.
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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".