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Record W3168482073 · doi:10.1021/acs.iecr.1c01091

Ethylene Polymerization Kinetics and Microstructure of Polyethylenes Made with Supported Metallocene Catalysts

2021· article· en· W3168482073 on OpenAlexaff
Saeid Mehdiabadi, Olivier Lhost, Aurélien Vantomme, João B. P. Soares

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2021
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMetallocenePolyethylenePolyolefinPost-metallocene catalystPolymerizationMaterials scienceEthylenePolymer chemistryChemical engineeringCatalysisCoordination polymerizationChemistryOrganic chemistryPolymerSolution polymerizationComposite material

Abstract

fetched live from OpenAlex

The microstructure of polyolefins made with a metallocene supported on a porous carrier is generally less homogeneous than when the polyolefin is made with the same unsupported metallocene. For instance, the molecular weight and chemical composition distributions of ethylene/1-olefin copolymers are often broader when they are made with a supported metallocene. In this article, a mathematical model is used to quantify this phenomenon. The polymerization of ethylene was investigated in parallel semi-batch reactors using a metallocene catalyst supported on an inorganic porous carrier. Ethylene pressure, polymerization temperature, and H2 concentration were the factors changed to study ethylene polymerization kinetics with this catalyst system. Modeling results showed that a three-site model was needed to describe the molecular weight distributions of the polyethylene samples made with this catalyst.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.270
Teacher spread0.232 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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