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Record W1985355992 · doi:10.1021/ma0516844

DFT Study of Ethylene and Propylene Copolymerization over a Heterogeneous Catalyst with a Coordinating Lewis Base

2005· article· en· W1985355992 on OpenAlexaff
Zygmunt Flisak, Tom Ziegler

Bibliographic record

VenueMacromolecules · 2005
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComonomerCopolymerPolymer chemistryEthyleneReactivity (psychology)CatalysisOlefin fiberLewis acids and basesTetrahydrofuranPolymerChemistryPropylene oxidePost-metallocene catalystMetalloceneMaterials scienceOrganic chemistryEthylene oxidePolymerizationSolvent

Abstract

fetched live from OpenAlex

The copolymerization of ethylene and propylene over a heterogeneous Ti(III) catalyst containing tetrahydrofuran (THF) as a Lewis base and MgCl 2 as a support has been studied by means of DFT. Two feasible models of active sites have been examined thoroughly, and one of them turned out to be favorable in terms of both catalytic activity and the microstructure of the resulting polymer. The external barriers of olefin insertion for this model range from 3.1 to 16.0 kcal/mol and are influenced by a variety of factors, such as the structure of the growing polymer chain and the nature of the incoming olefin as well as the orientation of the ligands around the titanium atom. Stochastic simulations performed on the basis of insertion and termination barriers provided us with the insight into the composition and microstructure of the copolymer as well as its molecular weight as a function of comonomer partial pressures. It is demonstrated that the reactivity of ethylene in the copolymerization process is significantly higher than that of propylene, which is consistent with known experimental data. Our results also indicate moderate regiospecificity and stereoselectivity toward propylene, depending on the partial pressures of the comonomers.

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 categoriesInsufficient 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 score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.218
Teacher spread0.209 · 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

Citations43
Published2005
Admission routes1
Has abstractyes

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