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Record W3036410957 · doi:10.1002/anie.202006586

Palladium‐Catalyzed Cross‐Coupling of Alkenyl Carboxylates

2020· article· en· W3036410957 on OpenAlexafffund
Joseph Becica, Oliver R. J. Heath, Cameron H. M. Zheng, David C. Leitch

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrophileCatalysisChemistryAlkeneCarboxylatePalladiumCatalytic cycleCombinatorial chemistryCoupling (piping)Base (topology)Oxidative coupling of methaneCoupling reactionLimitingOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Carboxylate esters have many desirable features as electrophiles for catalytic cross‐coupling: they are easy to access, robust during multistep synthesis, and mass‐efficient in coupling reactions. Alkenyl carboxylates, a class of readily prepared non‐aromatic electrophiles, remain difficult to functionalize through cross‐coupling. We demonstrate that Pd catalysis is effective for coupling electron‐deficient alkenyl carboxylates with arylboronic acids in the absence of base or oxidants. Furthermore, these reactions can proceed by two distinct mechanisms for C−O bond activation. A Pd0/II catalytic cycle is viable when using a Pd0 precatalyst, with turnover‐limiting C−O oxidative addition; however, an alternative pathway that involves alkene carbopalladation and β‐carboxyl elimination is proposed for PdII precatalysts. This work provides a clear path toward engaging myriad oxygen‐based electrophiles in Pd‐catalyzed cross‐coupling.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2020
Admission routes2
Has abstractyes

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