A Thermoresponsive Membrane for Chiral Resolution
Bibliographic record
Abstract
Abstract A novel thermoresponsive membrane for chiral resolution with high performance has been developed. The membrane exhibits chiral selectivity based on molecular recognition of beta‐cyclodextrin (β‐CD) and thermosensitivity based on the phase transition of poly(N‐isopropylacrylamide) (PNIPAM). Linear PNIPAM chains were grafted onto porous nylon‐6 membrane substrates by using a plasma‐graft pore‐filling polymerization method; the chains thus acted as microenvironmental adjustors for β‐CD molecules. β‐CD moieties were introduced into the linear PNIPAM chains by a chemical grafting polymerization method and acted as chiral selectors. The phase transition of grafted PNIPAM chains affects the microenvironment of β‐CD molecules and, thus, the association between β‐CD and guest molecules. The chiral selectivity of the prepared thermoresponsive membranes in chiral resolution operated at temperature below the lower critical solution temperature (LCST) of PNIPAM is higher than that of membranes with no thermosensitivity. Furthermore, the decomplexation ratio of enantiomer‐loaded thermoresponsive membranes in decomplexation at temperatures above the LCST is much higher than that of membranes with no thermosensitivity. Thus, by simply changing the operation temperature, high, selective chiral resolution and efficient membrane regeneration are achieved. The proposed membrane provides a new and efficient way to solve the difficult decomplexation problem of chiral solid membranes, which is highly attractive for chiral resolution.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".