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Record W2916230379 · doi:10.1021/acsomega.8b03501

Hybrid Bionanocomposites from Spent Hen Proteins

2019· article· en· W2916230379 on OpenAlexafffund
Muhammad Zubair, Jianping Wu, Aman Ullah

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat AgencyCanadian Poultry Research Council
KeywordsPlasticizerMaterials scienceExfoliation jointComposite materialCompression moldingBiopolymerNanocompositeChitosanPolymerIntercalation (chemistry)MontmorilloniteChemical engineeringMolding (decorative)ChemistryNanotechnologyMoldGrapheneOrganic chemistry

Abstract

fetched live from OpenAlex

Spent hens, a poultry by-product, have little economic value for processing and mostly end up in landfills. However, there are concerns over disposal of spent hens; therefore, it is pertinent to find out alternative uses that are environmentally sound. On the other hand, single-use plastic packaging is leading to a global environmental crisis. In this study, proteins were extracted from spent hen, plasticized, and processed into films by compression molding. The hybrid bionanocomposite films were successfully prepared using glycerol as a plasticizer, chitosan as a cross-linker, and varying concentrations of nanoclay as a nanoreinforcement. The effects of nanoreinforcements, plasticization, and cross-linking were then evaluated on thermal, mechanical, and barrier properties of the prepared bionanocomposite films. Various concentrations of nanoclay and chitosan were dispersed in the protein matrix. However, with the same plasticizer loading, the optimum addition of chitosan and nanoclay led to almost twofold increase in the mechanical strength, compared to neat protein films. The results indicated that at optimal conditions, a good intercalation and/or exfoliation of the protein biopolymers into clay interlayer galleries was observed leading to improved thermal, thermomechanical, and barrier properties. These hybrid bionanocomposite films have great future potential to be used in packaging and other 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.017

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations29
Published2019
Admission routes2
Has abstractyes

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