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Record W2010862322 · doi:10.1002/app.35477

Effectiveness of silane monomer on chitosan films and PCL‐based tri‐layer films

2011· article· en· W2010862322 on OpenAlexaff
Nusrat Sharmin, Ruhul A. Khan, Stéphane Salmieri, Dominic Dussault, Monique Lacroix

Bibliographic record

VenueJournal of Applied Polymer Science · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSilaneChitosanMaterials scienceUltimate tensile strengthScanning electron microscopeCompression moldingComposite materialPolycaprolactoneCastingLayer (electronics)PolymerPolymer chemistryChemical engineeringMold

Abstract

fetched live from OpenAlex

Abstract Chitosan films were prepared by casting from its 1% (w/w) solution. Tensile strength (TS), tensile modulus (TM), and elongation at break (Eb%) of chitosan films were found to be 30 MPa, 450 MPa, and 8%, respectively. Silane monomer (3‐aminopropyl tri‐methoxy silane) (1–7%, w/w) was added into the chitosan solution (1%, w/w), and films were casted. The highest TS (38 MPa) and TM (620 MPa) values were found at 5% (w/w) silane content films. Polycaprolactone (PCL) films were prepared by compression molding; the values of TS, TM, and Eb of PCL films were found to be 12 MPa, 226 MPa, and 70%, respectively. Using chitosan and silane containing chitosan films, PCL‐based tri‐layer films were prepared by compression molding. Chitosan (also silane‐incorporated chitosan) content in the tri‐layer films varied from 10 to 50% by weight. The values of TS and TM of the silane containing composites were found to be 21 and 410 MPa, respectively, which is significantly higher (40% TS and 34% TM) than control composites. Molecular interactions due to silane addition were supported by FT‐IR analysis. The surface of the films and the interface of the tri‐layer films were investigated by scanning electron microscope (SEM). © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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 teacher head, 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

Citations14
Published2011
Admission routes1
Has abstractyes

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