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Record W2884784892 · doi:10.5539/jmsr.v7n4p16

Preparation and Characterization of Carboxymethyl Cellulose Films with Embedded Essential Oils

2018· article· en· W2884784892 on OpenAlexvenueno aff
Atanu Biswas, Roselayne Ferro Furtado, Maria do Socorro Rocha Bastos, S. D. Benevides, Marília de Albuquerque Oliveira, Veera Boddu, H. N. Cheng

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
FundersCiência sem FronteirasConselho Nacional de Desenvolvimento Científico e TecnológicoU.S. Department of Agriculture
KeywordsCarboxymethyl celluloseMaterials scienceFood packagingCelluloseFood spoilageNutmegEugenolActive packagingChemical engineeringOrganic chemistryFood scienceChemistry

Abstract

fetched live from OpenAlex

There is current interest in using biobased materials to produce food packaging that can increase the shelf-lives of fruits and vegetables and minimize food spoilage in supermarkets and at the same time not generating plastic waste that causes long-term disposal problems. A good candidate for such materials is the polysaccharide, such as carboxymethyl cellulose (CMC), which is edible and biodegradable. In this work films were produced from two CMC materials with different degrees of substitution (DS) that encapsulated four different essential oils (eugenol, rosemary oil, coriander oil, and nutmeg oil) that are known to have beneficial properties for food applications. The mechanical properties, opacity, and water vapor permeation were evaluated. In general, the essential oil-embedded CMC with the two DS values behaved rather differently. In particular, the essential oil-embedded CMC with 0.7 DS degree of substitution gave stronger and more flexible films and may be more suited for use in food packaging.

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 score0.002

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 head0.027
GPT teacher head0.359
Teacher spread0.333 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

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