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Record W2942673332 · doi:10.1002/cctc.201900550

Hydrosilylative Reduction of Tertiary Amides to Amines Catalyzed by <i>N</i>‐(Phosphinoaryl)anilido Complexes of Iron and Cobalt

2019· article· en· W2942673332 on OpenAlexafffund
Dylan J. Hale, Luke J. Murphy, Robert McDonald, Michael J. Ferguson, Laura Turculet

Bibliographic record

VenueChemCatChem · 2019
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of AlbertaDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsHydrosilylationAmideCatalysisCobaltChemistryAlkylLigand (biochemistry)Medicinal chemistryRedoxOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The synthesis and structural characterization of low‐coordinate Fe(II) and Co(II) complexes supported by the monoanionic P,N‐ligand N ‐(2‐dicyclohexylphosphinophenyl)‐2,6‐diisopropylanilide are described. A three‐coordinate (P,N)Fe‐hexamethyldisilazide complex ( 2 ), and four‐coordinate (P,N)Fe‐ ( 3‐Fe ) and (P,N)Co‐alkyl ( 3‐Co ) complexes were evaluated as pre‐catalysts for the hydrosilylative reduction of amides with PhSiH 3 (5 mol % pre‐catalyst, 1 equiv. PhSiH 3 , 80 °C, 1–24 h). The Fe complex 2 proved to be more broadly effective for the reduction of a variety of tertiary amide substrates, and was shown to mediate the reduction of N , N ‐dibenzylbenzamide at a loading of 1 mol %, to achieve near quantitative formation of tribenzylamine in 1 h (80 °C). Complex 2 also proved effective for the hydrosilylation of tertiary amides under ambient conditions (5 mol % Fe, 24 h), which is a unique example of room temperature amide hydrosilylation mediated by an Fe catalyst without the need for photochemical activation. Given the widespread use of amide reduction protocols in synthesis, the development of efficient Fe‐based catalysts that operate under mild conditions is an important target.

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 score0.009
Threshold uncertainty score0.814

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)0.0000.000

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 head0.005
GPT teacher head0.217
Teacher spread0.211 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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