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Record W4306746312 · doi:10.1021/acscatal.2c04493

Palladium Catalyzed Conversion of Aryl Triflates to Acyl-DMAP Salts: A Mild and Versatile Approach to Carbonylations

2022· article· en· W4306746312 on OpenAlexafffund
Pierre‐Louis Lagueux‐Tremblay, Célestin Augereau, Pranav Nair, Kwan Ming Tam, Bruce A. Arndtsen

Bibliographic record

VenueACS Catalysis · 2022
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCarbonylationNucleophileChemistryArylElectrophilePalladiumCatalysisReductive eliminationTrifluoromethanesulfonateCombinatorial chemistryReagentOrganic chemistryAlleneAlkyl

Abstract

fetched live from OpenAlex

The use of metal catalysis to form potent acylating agents offers an important avenue to broaden the applicability of carbonylation reactions. However, the often disfavored reductive elimination of these reactive products presents a challenge, and has made these often high temperature, high pressure reactions and limited in their scope. Herein, we describe a mild and versatile palladium catalyzed carbonylative method to generate electrophilic acyl-DMAP (4-dimethylaminopyridine) salts by exploiting an alternative feature of these reactions: the counterion. In this, the weakly coordinating triflate anion is found to strongly destabilize Pd(II) toward irreversible reductive elimination of the reactive products, while ligand design can allow the rapid activation of aryl or vinyl triflates. Together, this provides a route to carry out palladium catalyzed carbonylations under exceptionally mild conditions, with low catalyst loading (0.5 mol % Pd), and with broadly available C(sp2)-triflate reagents. Coupling their formation with the addition of nucleophiles can be used to assemble a variety of products by carbonylation reactions, including those with palladium reactive functionalities.

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 score0.050
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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