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Record W1990040924 · doi:10.1021/om700452u

New Insight into the Role of the Metal Oxidation State in Controlling the Selectivity of the Cr-(SNS) Ethylene Trimerization Catalyst

2007· article· en· W1990040924 on OpenAlexafffund
Claire N. Temple, Sandro Gambarotta, Ilia Korobkov, Robbert Duchateau

Bibliographic record

VenueOrganometallics · 2007
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryDivalentCatalysisCationic polymerizationSelectivityEthyleneOxidation statePyridineChromiumInorganic chemistryMetalPolymer chemistryMedicinal chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The tri- and divalent complexes of the 2,6-bis(RSCH 2 )pyridine [R = Ph, Cy] ligands have been prepared. Upon activation with MAO, both species are catalysts for ethylene oligomerization of moderate activity. However, while the trivalent catalysts produced only 1-hexene, the divalent species gave a statistical distribution of oligomers. This clear difference in catalytic behavior indicates that the two oxidation states are not interconnected during the catalytic cycle as it happens instead with other oligomerization catalytic systems. The trivalent precursor is not reduced and the divalent is not oxidized. Treatment of the trivalent catalyst precursors with either MAO or other R 3 Al species afforded intractable materials. Instead, similar reactions with the divalent complexes gave new cationic species, which have been characterized by X-ray analysis. These complexes have preserved the divalent state of chromium during the reaction and still produce, upon further activation, a statistical distribution of oligomers. This reiterates the non-interconvertibility of the di- and trivalent oxidation states and the different degree of selectivity for which they are responsible.

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 categoriesnone
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 score0.038
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations65
Published2007
Admission routes2
Has abstractyes

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