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Record W4307172478 · doi:10.1002/cctc.202201231

Efficient Base‐Catalyzed Hydrosilylation of Tertiary Amides to Aldehydes

2022· article· en· W4307172478 on OpenAlexafffund
Joshua A. Clarke, Georgii I. Nikonov

Bibliographic record

VenueChemCatChem · 2022
Typearticle
Languageen
FieldChemistry
TopicAdvanced Synthetic Organic Chemistry
Canadian institutionsRegional Municipality of NiagaraBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCatalysisHydrosilylationOrganic chemistryTertiary amineBase (topology)Transition metalCombinatorial chemistry

Abstract

fetched live from OpenAlex

Abstract A chemoselective catalytic reduction of tertiary amides to aldehydes is reported. The catalytic system KOtBu/(EtO) 2 MeSiH has been found to be highly active in the conversion of tertiary amides to silylated hemiaminals, which after acidic work‐up yields the respective aldehydes under mild conditions. The use of amides containing α‐protons in this catalytic reaction resulted in elimination to furnish enamines characterized by 1 H‐NMR. This mild and selective process is compatible with nitriles, ethers, amines, and heterocyclic functionalities. However, competition experiments revealed poor tolerance for easily reducible functional groups, such as esters, ketones, and aldehydes. This transition metal‐free strategy presents a cheap and readily available process for the efficient reduction of tertiary amides to the corresponding aldehydes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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