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Record W1865666418 · doi:10.1002/cjce.22358

Synthesis and properties of polyethylene/TiO<sub>2</sub> nanocomposites using a vanadium catalyst

2015· article· en· W1865666418 on OpenAlexvenueno aff
Omer Y. Bakather, Mamdouh A. Al‐Harthi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
FundersKing Fahd University of Petroleum and Minerals
KeywordsPolyethyleneVanadiumEthyleneMaterials sciencePolypropyleneNanocompositeTitaniumCatalysisCrystallinityCopolymerPolymerizationTitanium dioxidePolymerPolymer chemistryChemical engineeringChemistryOrganic chemistryComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract In this study, a vanadium (III) complex bearing a salicylaldiminato ligand of the general formula [RN=CH(2,4‐ t Bu 2 C 6 H 2 O)]VCl 2 (THF) 2 , where R = 2,6‐ i Pr 2 C 6 H 3 , was used as a catalyst. Titanium dioxide doped with iron nanofillers was synthesized by a sol‐gel process and used to investigate the effect of nanofillers on ethylene homopolymer and ethylene/propylene copolymer properties. To the best of our knowledge, this is the first time titanium dioxide doped with iron has been used as a nanofiller in ethylene polymerization and ethylene/propylene copolymerization using a vanadium complex with methyl aluminum dichloride as a cocatalyst. Besides catalyst activity, the molecular mass ( M w ) of the obtained polymer, molecular mass distribution, copolymer composition, crystallinity, and thermal characteristics of polyethylene and polyethylene/polypropylene nanocomposites were investigated.

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)0.001
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 score0.009
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.190
Teacher spread0.172 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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