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Record W4402638281 · doi:10.1002/chem.202402391

Mapping Electrophile Chemoselectivity in DalPhos/Nickel <i>N</i>‐Arylation Catalysis: The Unusual Influence of Remote Sterics

2024· article· en· W4402638281 on OpenAlexafffund
Peter Fox, Jeongin Choi, Erin R. Johnson, Mark Stradiotto

Bibliographic record

VenueChemistry - A European Journal · 2024
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaKillam TrustsDalhousie UniversityResearch Nova ScotiaUniversities Space Research Association
KeywordsChemoselectivityElectrophileSteric effectsNickelCatalysisChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We disclose herein our evaluation of competitive (hetero)aryl-X (X: Br>Cl>OTf) reactivity preferences in bisphosphine/Ni-catalyzed C-N cross-coupling catalysis, using furfurylamine as a prototypical nucleophile, and employing DalPhos and DPPF as representative ancillary ligands with established efficacy. Beyond this general (pseudo)halide ranking, other intriguing structure-reactivity trends were noted experimentally, including the unexpected observation that bulky alkyl (e. g., R=tBu) substitution in para-R-aryl-X electrophiles strongly discourages (pseudo)halide reactivity relative to smaller substituents (e. g., nBu, Et, Me), despite being both remote from, and having a similar electronic influence on, the reacting C-X bond; such effects on nickel oxidative addition have not been documented previously and were not observed in our comparator reactions presented herein involving palladium. Density functional theory modeling of such PhPAd-DalPhos/Ni-catalyzed C-N cross-couplings revealed the origins of competitive turnover of C-Br over C-Cl, and possible ways in which bulky para-alkyl substitution might discourage net electrophile uptake/turnover, leading to inversion of halide selectivity.

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 score0.031
Threshold uncertainty score0.906

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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