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Record W2127512862 · doi:10.1002/anie.201507548

Kinetic Resolution of Azomethine Imines by Brønsted Acid Catalyzed Enantioselective Reduction

2015· article· en· W2127512862 on OpenAlexafffund
Amanda Bongers, Patrick J. Moon, André M. Beauchemin

Bibliographic record

VenueAngewandte Chemie International Edition · 2015
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Synthesis and Catalysis
Canadian institutionsCentre in Green Chemistry and Catalysis
FundersSt. Anna Children's Cancer Research InstituteNatural Sciences and Engineering Research Council of Canada
KeywordsEnantioselective synthesisKinetic resolutionChemistryImineCatalysisBrønsted–Lowry acid–base theoryAzomethine ylideEnantiomerCombinatorial chemistryStereochemistryOrganic chemistryCycloaddition

Abstract

fetched live from OpenAlex

Azomethine imines are valuable substrates in asymmetric catalysis, and can be precursors to β-amino carbonyl compounds and complex hydrazines. However, their utility is limited because complex and enantioenriched azomethine imines are often unavailable. Reported herein is a kinetic resolution of N,N'-cyclic azomethine imines by enantioselective reduction (s=13-43). This resolution was accomplished using a Brønsted acid catalyst, and represents the first example of the asymmetric reduction of azomethine imines. The pyrazolidinone product (up to 86 % ee) and the recovered azomethine imine (up to 99 % ee) can both be used to access the opposite enantiomers of valuable products.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.253
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2015
Admission routes2
Has abstractyes

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