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Record W2155505654 · doi:10.1002/mren.200700033

Simulation of Polymerization and Long Chain Branch Formation in a Semi‐Batch Reactor Using Two Single‐Site Catalysts

2008· article· en· W2155505654 on OpenAlexaff
Saeid Mehdiabadi, João B. P. Soares, Armenag H. Dekmezian

Bibliographic record

VenueMacromolecular Reaction Engineering · 2008
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolymerizationPolymer chemistryMaterials scienceTacticityElastomerThermoplastic elastomerCatalysisOlefin fiberOlefin polymerizationCopolymerReactivity (psychology)Kinetic chain lengthThermoplasticAnionic addition polymerizationChemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract We developed a mathematical model for the solution polymerization of olefins in a semi‐batch reactor with two single‐site catalysts. In the propylene polymerization case, our objective is to study the production of a thermoplastic elastomer using two catalysts, one capable of forming isotactic chains containing terminal vinyl bonds (macromonomers) and the other producing atactic chains while also being able to copolymerize macromonomers to form long chain branches. A similar thermoplastic elastomer can be produced by polymerizing ethylene and α‐olefin comonomers when the α‐olefin reactivity ratios of the two catalysts are significantly different. magnified image

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.215
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2008
Admission routes1
Has abstractyes

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