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Record W2017266260 · doi:10.1021/om801000v

In Silico Design of <i>C</i><sub>1</sub>- and <i>C</i><sub><i>s</i></sub>-Symmetric Fluorenyl-Based Metallocene Catalysts for the Synthesis of High-Molecular-Weight Polymers from Ethylene/Propylene Copolymerization

2009· article· en· W2017266260 on OpenAlexaff
Tebikie Wondimagegn, Dongqi Wang, Abbas Razavi, Tom Ziegler

Bibliographic record

VenueOrganometallics · 2009
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCopolymerEthyleneMetalloceneChemistryCyclopentadienyl complexPolymerPolymer chemistrySteric effectsCatalysisSandwich compoundPolymerizationPost-metallocene catalystOrganic chemistry

Abstract

fetched live from OpenAlex

C 1 - and C s -symmetric fluorenyl-based metallocenes have been used extensively as catalysts for the synthesis of high-molecular-weight polymers from ethylene and propylene homopolymerization. However, these same catalysts produce only low-molecular-weight polymers in ethylene/propylene copolymerization. We have shown in a recent study that the poor performance of fluorenyl-based C 1 -symmetric zirconocenes in ethylene/propylene polymerization is a result of electronic effects. Furthermore, we have also shown in another investigation that incorporating sterically demanding substituents in the 2- and 4- positions of C 2 -symmetric zirconocenes can significantly increase molecular weight in copolymerization. In the present study we shall use the same approach (modifying substituents on the cyclopentadienyl and fluorenyl ligands) to design C 1 - and C s -symmetric fluorenyl-based metallocenes that afford high-molecular-weight polymers from ethylene/propylene copolymerization.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.200
Teacher spread0.192 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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