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Record W4391486295 · doi:10.6000/1929-5995.2024.13.01

Physicochemical Properties of Films from Semirefined Carrageenan/TiO2 Integrated with Cinnamaldehyde Pickering Emulsion for Active Food Packaging

2024· article· en· W4391486295 on OpenAlexvenueno aff
Khadijah Husna Abd Hamid, Azilah Ajit, Azren Aida Asmawi, Mohd Hafiz Arzmi, Nurul Aini Mohd Azman

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceCinnamaldehydePickering emulsionEmulsionCarrageenanFood packagingActive packagingChemical engineeringComposite materialFood scienceOrganic chemistryChemistryEngineering

Abstract

fetched live from OpenAlex

Plastic waste has become a significant global environmental issue, particularly in the context of food packaging. In the present study, active packaging films were fabricated by integrating chitosan-stabilized cinnamaldehyde Pickering emulsion (PE) and titanium dioxide particles (TNPs) into the semirefined carrageenan (SRC) matrix. The impact of cinnamaldehyde PE and TNPs on the physical and mechanical attributes of the SRC films was explored. The integration of TNPs (3%, w/v) and 0.5% cinnamaldehyde PE revealed promising mechanical properties, with 21.86 MPa tensile strength and 34.21% of elongation at break value. The inclusion of TNPs and cinnamaldehyde PE led to enhancements in the moisture content and water solubility of the SRC films. The thermal stability of the film was marginally increased with 0.5% cinnamaldehyde PE. Scanning electron microscopy (SEM) revealed a uniform distribution of active compounds in the SRC matrix. The study findings highlight the potential of cinnamaldehyde PE and TNPs in active food packaging films as eco-friendly alternatives to conventional petrochemical-derived plastics 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 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 score0.004
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.325
Teacher spread0.291 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research Updates in Polymer ScienceSame topicNanocomposite Films for Food PackagingFrench-language works237,207