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Record W4379619179 · doi:10.1021/acs.joc.3c00463

Ferrocenium Boronic Acid Catalyzed Deoxygenative Coupling of Alcohols with Carbon- and Nitrogen-Based Borate and Silane Nucleophiles

2023· article· en· W4379619179 on OpenAlexafffund
Jake J. Blackner, Deirdre M. Rooney, Joshua W. Hollett, J. Adam McCubbin

Bibliographic record

VenueThe Journal of Organic Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryNucleophileCatalysisSilanesOrganic chemistryBoronic acidAllylic rearrangementAlcoholCombinatorial chemistrySilane

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide A boronic acid catalyzed carbon–carbon and carbon–nitrogen bond-forming reaction for the functionalization of various π-activated alcohols has been developed. Ferrocenium boronic acid hexafluoroantimonate salt was identified as an effective catalyst in the direct deoxygenative coupling of alcohols with a variety of potassium trifluoroborate and organosilane nucleophiles. In a comparison between these two classes of nucleophiles, the use of organosilanes leads to higher reaction yields, increased diversity of the alcohol substrate scope, and high E / Z selectivity. Furthermore, the reaction proceeds under mild conditions and yields up to 98%. Computational studies provide a rationalization for a mechanistic pathway for the retention of E / Z stereochemistry when E or Z alkenyl silanes are used as nucleophiles. This methodology is complementary to existing methodologies for deoxygenative coupling reactions involving organosilanes, and it is effective with a variety of organosilane nucleophile sub-types, including allylic, vinylic, and propargylic trimethylsilanes.

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 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 score0.003
Threshold uncertainty score0.683

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)0.0000.000

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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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