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Record W4206351656 · doi:10.3390/polym14030383

Preparation and Characterization of Antibacterial Films with Eggshell-Membrane Biopolymers Incorporated with Chitosan and Plant Extracts

2022· article· en· W4206351656 on OpenAlexaff
Brian Cameron Wooding Webb, Steven P. Rafferty, Andrew J. Vreugdenhil

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsTrent University
Fundersnot available
KeywordsEggshell membraneChitosanAbsorption (acoustics)MembraneLysozymeAntibacterial activityNuclear chemistryMaterials scienceChromatographyAbsorption of waterChemical engineeringChemistryBacteriaOrganic chemistryBiochemistryComposite materialBiology

Abstract

fetched live from OpenAlex

A series of films containing chitosan (CS), eggshell membrane (ESM), soluble eggshell membrane (SEP), and plant extracts from Thymus vulgaris and Origanum valgare were prepared with varying concentrations and compositions. These novel films were characterized extensively with respect to film thickness and uniformity, solution absorption, degradation, microenvironmental pH, and antibacterial properties. All the films were flexible with appropriate mechanical stability. After 48 h of soaking in a lysozyme solution, all the films degraded 64 ± 4%, which would be expected to allow for the release of the plant extracts. The plant extracts on their own showed a pH of approximately 4, with the blended films having microenvironmental pHs from approximately 6.4–7.0, which would be expected to promote wound healing. A CS-ESM-SEP film with 5% of each plant extract inhibited almost all E. coli growth in liquid cultures and had no detriments to fluid absorption. Fluid absorption was approximately 100–150% by weight for all the films. The incorporation of SEP and plant extracts to a CS-ESM film provides a promising and novel method for the incorporation of SEP and antibacterial agents in a film with no detriment to wound fluid absorption or film degradation.

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 score0.005
Threshold uncertainty score0.628

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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