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Record W2131105815 · doi:10.1002/app.38224

An investigation into surface modification of polyethylene films for hydrophilicity enhancement by catalytic ozonation

2012· article· en· W2131105815 on OpenAlexaff
Erlita Mastan, Jiangning Wu, Huu Doan

Bibliographic record

VenueJournal of Applied Polymer Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAqueous solutionOzonePolyethyleneContact angleCatalysisPeroxideChemical engineeringUltimate tensile strengthChemistryMaterials scienceDecompositionPolymer chemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract For a conventional polyethylene film, ozonation was conducted to increase its surface hydrophilicity. Copper sulfate was found effective as a catalyst in aqueous ozonation to increase the generation of surface peroxides significantly, which is the essential step for hydrophilicity enhancement. The percentage increase in peroxide generation could range from 41% to 58% by aqueous catalytic ozonation over its noncatalyzed gaseous counterpart. The optimum concentration of copper sulfate was found to be 0.05 g/L. After adding the catalyst, the length of ozonation could be significantly reduced so that the bulk mechanical strength of the films, represented by tensile strength, could be well preserved after ozonation. A novel kinetic model for the aqueous ozonation was proposed. This model was developed by combing the reaction mechanism of gaseous ozonation of polyethylene films with that of ozone self‐decomposition in water. The peroxide generation also increased with the applied ozone dose. The surface morphology and contact angle of the film were all examined before and after ozonation. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

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 score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.022
GPT teacher head0.306
Teacher spread0.284 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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