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Record W4400097935 · doi:10.1038/s41467-024-49681-4

Phase transfer catalysts shift the pathway to transmetalation in biphasic Suzuki-Miyaura cross-couplings

2024· article· en· W4400097935 on OpenAlexafffund
Yao Shi, Joshua S. Derasp, Tristan Maschmeyer, Jason E. Hein

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsStructural Genomics ConsortiumUniversity of British Columbia
FundersNatural Resources CanadaCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaAdvanced Research Projects AgencyDefense Advanced Research Projects Agency
KeywordsTransmetalationCatalysisPalladiumChemistryElectrophilePhase (matter)Combinatorial chemistrySubstrate (aquarium)Coupling reactionAqueous two-phase systemOrganic chemistryBiology

Abstract

fetched live from OpenAlex

The Suzuki-Miyaura coupling is a widely used C-C bond forming reaction. Numerous mechanistic studies have enabled the use of low catalyst loadings and broad functional group tolerance. However, the dominant mode of transmetalation remains controversial and likely depends on the conditions employed. Herein we detail a mechanistic study of the palladium-catalyzed Suzuki-Miyaura coupling under biphasic conditions. The use of phase transfer catalysts results in a remarkable 12-fold rate enhancement in the targeted system. A shift from an oxo-palladium based transmetalation to a boronate-based pathway lies at the root of this activity. Furthermore, a study of the impact of different water loadings reveals reducing the proportion of the aqueous phase increases the reaction rate, contrary to reaction conditions typically employed in the literature. The importance of these findings is highlighted by achieving an exceptionally broad substrate scope with benzylic electrophiles using a 10-fold reduction in catalyst loading relative to literature precedent.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.022
GPT teacher head0.357
Teacher spread0.335 · 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

Citations17
Published2024
Admission routes2
Has abstractyes

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