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Record W2947718954 · doi:10.1021/acsomega.9b00251

Polyelectrolyte vs Polyampholyte Behavior of Composite Chitosan/Gelatin Films

2019· article· en· W2947718954 on OpenAlexafffund
Daniel Pinto Ramos, Sharon Sarjinsky, Moien Alizadehgiashi, Juri Möbus, Eugenia Kumacheva

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiopolymerPolyelectrolyteGelatinChitosanChemical engineeringIonic bondingSwellingDissolutionMaterials sciencePolymerComposite numberIonic strengthPolymer chemistryCoacervateChemistryIonComposite materialOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

Composite films of proteins and polysaccharides have a broad range of biomedical and food packaging applications, in which they are frequently exposed to fluid environments with varying ionic strengths. In the present work, we report the behavior of biopolymer films derived from chitosan (Ch), gelatin (GEL), and Ch/GEL mixture in salt solutions with varying concentrations and ion charges. The swelling and dissolution of the Ch films reduced with increasing salt concentration due to the polyelectrolyte behavior of this biopolymer, while the GEL films displayed a polyampholyte behavior, in which film swelling and dissolution were enhanced in salt solutions. Composite Ch/GEL films followed the behavior of GEL. The release of small ionic and zwitter-ionic molecules from the films was enhanced in ionic solutions due to the screened attraction between these molecules and the polymer matrix. These results provide insight into the behavior of protein/polysaccharide films in varying ionic environments, thus enabling enhanced design of biomaterials for a broad range of applications.

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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Same venueACS OmegaSame topicNanocomposite Films for Food PackagingFrench-language works237,207