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Record W2922262277 · doi:10.3390/pr7030157

Viscoelastic Properties of Crosslinked Chitosan Films

2019· article· en· W2922262277 on OpenAlexafffund
Joseph Khouri, Alexander Penlidis, Christine Moresoli

Bibliographic record

VenueProcesses · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCitric acidGlutaraldehydeChitosanCovalent bondViscoelasticityDynamic mechanical analysisMaterials scienceChemical engineeringIonic bondingPolymer chemistryGlass transitionPolymerModulusNuclear chemistryChemistryComposite materialOrganic chemistryIon

Abstract

fetched live from OpenAlex

Chitosan films containing citric acid were prepared using a multi-step process called heterogeneous crosslinking. These films were neutralized first, followed by citric acid addition, and then heat treated at 150 °C/0.5 h in order to potentially induce covalent crosslinking. The viscoelastic storage modulus, E′, and tanδ were studied using dynamic mechanical analysis, and compared with neat and neutralized films to elucidate possible crosslinking with citric acid. Films were also prepared with various concentrations of a model crosslinker, glutaraldehyde, both homogeneously and heterogeneously. Based on comparisons of neutralized films with films containing citric acid, and between citric acid films either heat treated or not heat treated, it appeared that the interaction between chitosan and citric acid remained ionic without covalent bond formation. No strong evidence of a glass transition from the tanδ plots was observable, with the possible exception of heterogeneously crosslinked glutaraldehyde films at temperatures above 200 °C.

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 score0.005

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)0.0020.000

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 head0.012
GPT teacher head0.222
Teacher spread0.209 · 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

Citations68
Published2019
Admission routes2
Has abstractyes

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