Enantiomeric separation of tryptophan via novel chiral polyamide composite membrane
Bibliographic record
Abstract
Abstract The separation of chiral drugs continues to pose a significant challenge. However, in recent years, the emergence of membrane‐based chiral separation has shown promising effectiveness due to its environmentally friendly, energy‐efficient, and cost‐effective characteristics. In this study, we prepared chiral composite membrane via interfacial polymerization (IP), utilizing β‐cyclodextrin (β‐CD) and piperazine (PIP) as mixed monomers in the aqueous phase. The chiral separation process was facilitated by β‐CD, serving as a chiral selective agent. The resulting membrane were characterized using SEM, FT‐IR, and XPS. Subsequently, the chiral separation performance of the membrane for DL‐tryptophan (Trp) was investigated. Lastly, the water flux, dye rejection, and stability of the membrane were also examined. The results showed that the optimized chiral PIP0.5β‐CD0.5 membrane achieved an enantiomeric excess percentage (ee%) of 43.0% for D‐Trp, with a solute flux of 66.18 nmol·cm−2·h−1, and maintained a good chiral separation stability. Additionally, the membrane demonstrated positive performance in the selective separation of mixed dyes, allowing for steady operation over a long period of time. This study offers fresh insights into membrane‐based chiral separations.
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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".