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Rendering Rayon Fibres Antimicrobial and Thermal-Responsive via Layer-by-Layer Self-Assembly of Functional Polymers

2011· article· en· W2064181633 on OpenAlexafffund
Yuan Feng Pan, Hui Xiao

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaGuangxi University
KeywordsLower critical solution temperaturePolymerMaterials scienceChemical engineeringVinyl alcoholAqueous solutionCationic polymerizationPolymer chemistryLayer by layerDissolutionLayer (electronics)Composite materialChemistryOrganic chemistryCopolymer

Abstract

fetched live from OpenAlex

A thermal-responsive polymer was prepared by partially acetalyzing poly(vinyl alcohol) (PVA). The completely reversible polymer aggregation and dissolution occur above and below a low critical solution temperature (LCST) for the aqueous solution of the modified PVA. The partially acetalized PVA (APVA) with higher molecular weight and higher degree of acetalysis exhibited a lower LCST transition and was used as an anionic polymer for polymer complexation. Water-soluble polymer, cationic polyhexamethylene guanidine hydrochloride (CPHGH) with antimicrobial property, was also prepared. In conjunction with APVA, CPHGH created the unique antimicrobial polymer multilayers on the surfaces of rayon fibres via layer by layer (LbL) assembly. AFM images revealed that the particles generated by multilayers became larger after the material was treated at 60°C; while the roughness of the surfaces was increased as the layer number increased and then decreased. Moreover, antimicrobial tests also demonstrated that the rayon fiber assembled with (CPHGH/APVA) multilayers exhibited higher antimicrobial activity against E. coli and s. aureus.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.072
GPT teacher head0.337
Teacher spread0.265 · 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
Published2011
Admission routes2
Has abstractyes

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