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Record W4376644402 · doi:10.3390/coatings13050924

Plasma-Deposited Organosilicon Hydrophobic Coatings on Cellulosic Materials for Wet Packaging Applications

2023· article· en· W4376644402 on OpenAlexafffund
Jacopo Profili, Sara Babaei, M. Al Rashidi, Annie Dorris, Siavash Asadollahi, A. Sarkissian, Luc Stafford

Bibliographic record

VenueCoatings · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsPlasmionique (Canada)FPInnovationsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPlasmioniqueFPInnovations
KeywordsOrganosiliconCoatingInductively coupled plasmaFood packagingMaterials scienceOctamethylcyclotetrasiloxaneChemical engineeringContact anglePolydimethylsiloxaneOrganic chemistryChemistryPlasmaComposite materialPolymer chemistry

Abstract

fetched live from OpenAlex

Non-toxic organosilicon coatings with hydrophobic properties were deposited on Kraft paper films using atmospheric-pressure dielectric barrier discharge. In order to assess the applicability of the plasma-deposited coating in the food packaging industry, its stability in different liquids (i.e., food simulants) was studied. Chemical analyses reveal that the food simulants, namely, de-ionized water, acetic acid, ethanol, and heptane, do not significantly alter the chemical composition or the hydrophobicity of the coatings. Based on inductively coupled plasma-optical emission spectroscopy analyses, the amount of Si released from the plasma-deposited coating is less than that typically detected in tap water. Overall, the results obtained suggest that hydrophobic plasma-deposited organosilicon coatings have great potential for use in food and wet packaging applications, especially considering their environmentally friendly character.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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.

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

Citations13
Published2023
Admission routes2
Has abstractyes

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