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Record W2910596971 · doi:10.1002/app.47376

Study on single and binary catalytic systems of pyridine‐imine catalysts based on nickel and iron in synthesis of reactor blends and low‐density polyethylene nanocomposites

2019· article· en· W2910596971 on OpenAlexaff
Mohsen Mogheiseh, Gholamhossein Zohuri, Mostafa Khoshsefat

Bibliographic record

VenueJournal of Applied Polymer Science · 2019
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Alberta
FundersFerdowsi University of Mashhad
KeywordsMaterials sciencePolyethyleneCrystallinityCatalysisNanocompositeThermal stabilityPolymer chemistryPyridineMonomerLow-density polyethyleneThermogravimetric analysisPolymerizationChemical engineeringComposite materialPolymerChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT In the presence of modified methylaluminoxane as cocatalyst, the behavior of a binary catalytic system based on pyridine‐imine nickel ( N ) and iron ( F ) catalysts was evaluated in order to reach a proper mixture of polyethylene (PE). A computational study along with kinetic profile suggested that the catalyst F with higher electron affinity ( A ) and electrophilicity (ω) in the methyl cationic active center and stronger interaction with the monomer led to high integrated monomer consumption and higher activity. In addition, the samples produced by the mixture of catalysts showed a higher value of [19.4 × 10 4 g (PE) mol (Fe+Ni) −1 h −1 )], melting point (127.8 °C), and crystallinity extent (41.29%) than the samples produced by the single catalysts. The addition of multiwalled carbon nanotubes (MWCNT) into the polymerization media reduced the activity of catalysts [from 7.50 × 10 4 to 0.66 × 10 4 g (PE) mol (Fe+Ni) −1 h −1 ] and the thermal properties of the low‐density polyethylene nanocomposite samples. However, the sample containing 2.33% MWCNT 20‐30 improved the total thermal stability of the neat polyethylene blend up to 400 °C. Scanning electron microscope images of the samples demonstrated irregular to virtually uniform morphologies were obtained through the in situ and solution‐mixing techniques. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47376.

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 score0.010
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.216
Teacher spread0.207 · 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
Published2019
Admission routes1
Has abstractyes

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