Molecularly imprinted nanofiber membranes from modified polysulfone bearing aldehyde moiety
Bibliographic record
Abstract
It is of interest to directly convert polymeric materials into molecularly imprinted nanofiber membranes by simultaneously applying an electrospray deposition and an alternative molecular imprinting. The membranes thus obtained was expected to show not only higher permselectivity but also higher flux value. To this end, molecularly imprinted nanofiber membranes were prepared from modified polysulfone bearing aldehyde moiety by adopting enantiomerically pure N-α-benzyloxycarbonyl-D-glutamic acid (Z-D-Glu) or N-α-benzyloxycarbonyl-L-glutamic acid (Z-L-Glu) as a print molecule. The fiber diameters of the molecularly imprinted nanofiber membranes thus obtained were 177 ± 64 nm. Enantioselective transport was studied by using concentration difference or potential difference as a driving force for membrane transport. The results obtained in the present study suggested that molecularly imprinted nanofiber membranes enhanced both permselectivity and flux, which are generally perceived to show a trade-off relationship. The flux for the present molecularly imprinted nanofiber membranes were about two orders of magnitude higher than the usual molecularly imprinted membranes, while the molecularly imprinted nanofiber membranes also showed permselectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".