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Record W3209939661 · doi:10.32920/ryerson.14655726.v1

An Investigation Into Surface Modificatin Of Polyethylene Film By Ozonation

2021· preprint· en· W3209939661 on OpenAlexaff
Dipak P. Patel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicPolymer Nanocomposite Synthesis and Irradiation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsContact angleFourier transform infrared spectroscopyUltimate tensile strengthOzoneLinear low-density polyethylenePolymerPeroxideAqueous solutionPolyethyleneAqueous two-phase systemPolyethylene terephthalateMaterials scienceChemistryHydrogen peroxideNuclear chemistryChemical engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

<p>The aim of this study was to investigate the hydrophilic modifications of the polymer films by ozonation. In this study, the polymer films (LD+LLDPE) were ozonated in gas phase and in aqueous phase, respectively. The surfaces of the polymer films were investigated in terms of peroxide generation, contact angle measurement, Fourier transform infrared (FTIR) spectroscopy and tensile strength measurements. </p> <p>Experimental results indicated that the amount of peroxide groups generated increased with ozonation time and applied ozone dose. It was also observed that the efficiency of ozonation was similar for gas phase ozonation, and aqueous ozonation. Catalyst screening revealed that Fe (III) and Cu (II) were both effective in accelerating peroxide generation. </p> <p>The hydrophilicity improvement of the film after ozonation was confirmed by contact angle measurements and Fourier transform infrared measurements. The LD+LLDPE films kept good tensile strength after ozonation. Even after 120 min ozonation at 1.0 wt% applied ozone dose, 85% of the tensile strength still remained. </p> <p>The application of catalyst to modify polymer film is the first of its kind. In this study, the approach proved to be successful for LD+LLDPE films.</p>

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 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 score0.014
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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