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Record W2765435617 · doi:10.1080/10610278.2017.1392521

Crown-ether-modified cyclic dipeptides as supramolecular chiral catalysts

2017· article· en· W2765435617 on OpenAlexafffund
Christopher Bérubé, Normand Voyer

Bibliographic record

VenueSupramolecular chemistry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesFonds de recherche du Québec – Nature et technologiesUniversité Laval
KeywordsChemistryCrown etherSupramolecular chemistryCatalysisEtherCrown (dentistry)Organic chemistryCombinatorial chemistryPolymer chemistryCrystal structureIon

Abstract

fetched live from OpenAlex

Christopher Bérubéa & Normand Voyera*a Département de chimie and PROTEO, Université Laval , Québec, Canada * Corresponding author: Email: Normand.voyer@chm.ulaval.ca AbstractWith the objective to develop supramolecular catalysts for useful chemical transformations, we report here a rapid and efficient solid-phase synthesis of novel cyclic dipeptides (crown-CDPs) with a diversity of L-DOPA derived crown ether substituents and stereochemistry. We prepared and characterized 14 crown-CDPs and evaluated their efficiency as supramolecular epoxidation catalysts in a water/hexanes biphasic system. Yields increase significantly in the presence of the crown-CDPs, though enantioselectivity depends on the nature of the substituents. The results reported constitute a useful approach for chiral epoxides of interest and further illustrate the potential of cyclic peptides as supramolecular catalysts.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.260
Teacher spread0.253 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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