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Record W1978650595 · doi:10.1016/j.carbpol.2012.07.037

Mechanical and barrier properties of nanocrystalline cellulose reinforced chitosan based nanocomposite films

2012· article· en· W1978650595 on OpenAlexafffund
Avik Khan, Ruhul A. Khan, Stéphane Salmieri, Canh Le Tien, Bernard Riedl, Jean Bouchard, Grégory Chauve, Victor Tan, Musa R. Kamal, Monique Lacroix

Bibliographic record

VenueCarbohydrate Polymers · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcGill UniversityUniversité LavalFPInnovationsArmand Frappier MuseumInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsChitosanNanocompositeMaterials scienceUltimate tensile strengthSwellingScanning electron microscopeCelluloseNanocrystalline materialComposite materialChemical engineeringCastingNanotechnology

Abstract

fetched live from OpenAlex

Nanocrystalline cellulose (NCC) reinforced chitosan-based biodegradable films were prepared by solution casting. The NCC content in the films was varied from 1 to 10% (dry wt. basis). It was found that the tensile strength (TS) of the nanocomposite films with 5% (w/w) NCC content was optimum with an improvement of 26% compared to the control chitosan films. Incorporation of NCC also significantly improved barrier properties. Water vapor permeability (WVP) of the chitosan/NCC films was decreased by 27% for the optimum 5% (w/w) NCC content. Swelling studies revealed a decrease in water uptake of the NCC-reinforced chitosan films. Analyses of thermal properties showed no significant effect of NCC whereas X-ray diffraction studies confirmed the appearance of crystalline peaks in the nanocomposite films. Surface morphology of the films was investigated by scanning electron microscopy and it was found that NCC was dispersed homogenously into chitosan matrix.

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 score0.002
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 communication0.0000.000
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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

Citations680
Published2012
Admission routes2
Has abstractyes

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