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Molecularly Imprinted Chitin Nanofiber Membranes: Multi-Stage Cascade Membrane Separation within the Membrane

2016· article· en· W2532318450 on OpenAlexvenueno aff
Kenta Shiomi, Masakazu Yoshikawa

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2016
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneChitinNanofiberChemistryCascadeChromatographyMaterials scienceChitosanNanotechnologyBiochemistry

Abstract

fetched live from OpenAlex

Molecularly imprinted nanofiber membranes were fabricated from chitin and print molecule of phenylalanine derivative by simultaneously applying an alternative molecular imprinting and an electrospinning. The D-enantiomer imprinted nanofiber membrane preferentially incorporated the D-enantiomer and selectively transported D-enantiomer and vice versa. The permselectivity was exponentially increased with the increase in the membrane thickness, implying that multi-stage cascade membrane separation was carried out within the nanofiber membrane. The present study led to the conclusion that a molecularly imprinted nanofiber membrane is one of suitable membrane forms for the separation membrane with relatively high flux and 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.321
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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