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Record W4388574836 · doi:10.18280/ijdne.180520

Fabrication and Characterization of Chitosan Film Incorporated with ZnO and Patchouli Oil for Food Packaging

2023· article· en· W4388574836 on OpenAlexvenueno aff
Yulianto Yulianto, Julinawati Julinawati, Haya Fathana, ⁠Rahmi ⁠Rahmi

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
Fundersnot available
KeywordsFabricationChitosanCharacterization (materials science)Food packagingMaterials sciencePatchouliNanotechnologyProcess engineeringChemical engineeringFood scienceEngineeringChemistryEssential oil

Abstract

fetched live from OpenAlex

This study aimed to fabricate and analyze a chitosan-based film incorporating ZnO and patchouli oil, and to elucidate its physicochemical and bioactive properties.The film was synthesized via the phase inversion method, utilizing 1 g of chitosan, 0.15 g of ZnO, and 0.25 mL of patchouli oil.An enhancement in the mechanical properties of the resultant film was observed, with the tensile strength escalating from an initial 2.59 kgf/mm 2 to 25.28 kgf/mm 2 .Fourier transform infrared (FTIR) spectroscopy affirmed the successful integration of ZnO and patchouli oil within the chitosan matrix.X-ray diffraction (XRD) analyses indicated a decrease in the crystallinity of the chitosan film post-addition of ZnO and patchouli oil.Furthermore, the modified chitosan film exhibited augmented antibacterial activity, with the inhibition zone diameters against Staphylococcus aureus and Escherichia coli expanding from 0 mm to 7.75 mm and 9.55 mm, respectively.Practical application of this chitosan-ZnO-patchouli oil film as a cover for grapes demonstrated its efficacy in preserving the freshness of the fruit over an extended period in comparison to conventional plastic.The results suggest that this modified chitosan film presents potential as a novel, natural, high-strength, antibacterial packaging material.

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)0.000
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 score0.215
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicNanocomposite Films for Food PackagingFrench-language works237,207