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Record W3004833542 · doi:10.1002/ejic.201901315

Structure‐Activity Relationships for Bis(phenolate‐ether) Zr/Hf Propene Polymerization Catalysts

2020· article· en· W3004833542 on OpenAlexaff
Eric N. T. Cuthbert, Antonio Vittoria, Roberta Cipullo, Vincenzo Busico, Peter H. M. Budzelaar

Bibliographic record

VenueEuropean Journal of Inorganic Chemistry · 2020
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChemistryPropeneSteric effectsCatalysisLigand (biochemistry)EtherPolymerizationRegioselectivityStereoselectivityLinkerPolymer chemistryStereochemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

A series of 20 bis(phenolate‐ether) ligands, in combination with Zr and Hf, was tested for performance in propene polymerization, focusing on molecular weight, stereoregularity and regioregularity of the resulting polymer. Ligand variation covers length of the aliphatic linker between the ligand halves, as well as steric bulk of the groups ortho to the phenolate oxygen. The linker length has a dramatic effect on MW: two‐carbon linkers produce oligomers (M n < 2.5 kDa) while three‐ and four‐carbon linkers generate much higher MW (M n typically 50–500 kDa). Stereoselectivity can be tuned using large, flat substituents in the o ‐phenolate position; tuning of regioselectivity is much harder. Hf catalysts are slower than their Zr analogs and do not work well with MAO/BHT (BHT = 2,6‐di‐ tert ‐butyl‐4‐methylphenol); they are generally more selective (MW, stereo and regio). Density functional calculations agree fairly well with observed selectivities, supporting the involvement of a fac/fac coordinated active species. These O4 catalysts are considerably more flexible than e.g. metallocenes, making accurate prediction of PP microstructure a significant challenge.

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)0.001
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.029
GPT teacher head0.208
Teacher spread0.178 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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