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Record W2950824263 · doi:10.1055/s-0037-1611853

An Old Dog with New Tricks: Enjoin Wolff–Kishner Reduction for Alcohol Deoxygenation and C–C Bond Formations

2019· article· en· W2950824263 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSynlett · 2019
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsnot available
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDeoxygenationChemistryHydrazoneCatalysisCoupling reactionMedicinal chemistryAlcoholOrganic chemistryConjugateProtonation

Abstract

fetched live from OpenAlex

The Wolff–Kishner reduction, discovered in the early 1910s, is a fundamental and effective tool to convert carbonyls into methylenes via deoxygenation under strongly basic conditions. For over a century, numerous valuable chemical products have been synthesized by this classical method. The reaction proceeds via the reversible formation of hydrazone followed by deprotonation with the strong base to give an N-anionic intermediate, which affords the deoxygenation product upon denitrogenation and protonation. By examining the mechanistic pathway of this century old classical carbonyl deoxygenation, we envisioned and subsequently developed two unprecedented new types of chemical transformations: a) alcohol deoxygenation and b) C–C bond formations with various electrophiles including Grignard-type reaction, conjugate addition, olefination, and diverse cross-coupling reactions. 1 Introduction 2 Background 3 Alcohol Deoxygenation 3.1 Ir-Catalyzed Alcohol Deoxygenation 3.2 Ru-Catalyzed Alcohol Deoxygenation 3.3 Mn-Catalyzed Alcohol Deoxygenation 4 Grignard-Type Reactions 4.1 Ru-Catalyzed Addition of Hydrazones with Aldehydes and Ketones 4.2 Ru-Catalyzed Addition of Hydrazone with Imines 4.3 Ru-Catalyzed Addition of Hydrazone with CO2 4.4 Fe-Catalyzed Addition of Hydrazones 5 Conjugate Addition Reactions 5.1 Ru-Catalyzed Conjugate Addition Reactions 5.2 Fe-Catalyzed Conjugate Addition Reactions 6 Cross-Coupling Reactions 6.1 Ni-Catalyzed Negishi-type Coupling 6.2 Pd-Catalyzed Tsuji–Trost Alkylation Reaction 7 Other Reactions 7.1 Olefination 7.2 Heck-Type Reaction 7.3 Ullmann-Type Reaction 8 Conclusion and Outlook

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.

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 score0.381
Threshold uncertainty score0.492

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 communication0.0000.001
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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