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Record W2271860477 · doi:10.1038/ncomms8508

Decarbonylative organoboron cross-coupling of esters by nickel catalysis

2015· article· en· W2271860477 on OpenAlexfundno aff
Kei Muto, Junichiro Yamaguchi, Djamaladdin G. Musaev, Kenichiro Itami

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsnot available
FundersMinistry of Education, Culture, Sports, Science and TechnologyJapan Society for the Promotion of ScienceDivision of ChemistryQueen's UniversityExploratory Research for Advanced TechnologyNational Science Foundation
KeywordsElectrophileNucleophileChemistryHalideNickelCombinatorial chemistryCatalysisCoupling reactionOrganic chemistryOrganoboron compoundsMoleculeSuzuki reactionPalladium

Abstract

fetched live from OpenAlex

The Suzuki-Miyaura cross-coupling is a metal-catalysed reaction in which boron-based nucleophiles and halide-based electrophiles are reacted to form a single molecule. This is one of the most reliable tools in synthetic chemistry, and is extensively used in the synthesis of pharmaceuticals, agrochemicals and organic materials. Herein, we report a significant advance in the choice of electrophilic coupling partner in this reaction. With a user-friendly and inexpensive nickel catalyst, a range of phenyl esters of aromatic, heteroaromatic and aliphatic carboxylic acids react with boronic acids in a decarbonylative manner. Overall, phenyl ester moieties function as leaving groups. Theoretical calculations uncovered key mechanistic features of this unusual decarbonylative coupling. Since extraordinary numbers of ester-containing molecules are available both commercially and synthetically, this new 'ester' cross-coupling should find significant use in synthetic chemistry as an alternative to the standard halide-based Suzuki-Miyaura 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 score0.008

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 communication0.0000.001
Open science0.0010.001
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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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

Citations281
Published2015
Admission routes1
Has abstractyes

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