MétaCan
Menu
Back to cohort
Record W2727093357 · doi:10.1002/pola.28685

Backbone rearrangement during olefin capture as the rate limiting step in molecular olefin polymerization catalysis and its effect on comonomer affinity

2017· article· en· W2727093357 on OpenAlexaff
Francesco Zaccaria, Roberta Cipullo, Peter H. M. Budzelaar, Vincenzo Busico, Christian Ehm

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComonomerChemistryMonomerOlefin fiberCatalysisPolymer chemistryCopolymerPolymerizationInsertion reactionLimitingSelectivityPhotochemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT Available experimental data for several metallocenes indicate that the ethene/propene copolymerization ratio r c can be much more temperature dependent than would be expected if competing insertion transition states (TS) are rate limiting. Detailed exploration of the reaction paths reveals in several cases the existence of a “capture‐like” transition state before the actual insertion, with free energies close to the insertion TS. Movement around these transition states does not just involve monomer and chain, but also clear distortion of the ligand skeleton to allow entry of the monomer. Taking these additional TSs into account leads to much improved agreement with experiment for a series of metallocenes and a constrained geometry catalyst system. Depending on catalyst and temperature, selectivity is determined by competing insertion/insertion, capture/insertion or capture/capture. It seems likely that this is a common situation especially for highly efficient catalysts, complicating (but not preventing) prediction of copolymerization performance. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017 , 55 , 2807–2814

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 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 score0.013
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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

Citations46
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Polymer Science Part A Polymer ChemistrySame topicOrganometallic Complex Synthesis and CatalysisFrench-language works237,207