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Record W3126137416 · doi:10.1002/cctc.202001862

Cover Feature: Mechanistic Insights into Fe Catalyzed α‐C−H Oxidations of Tertiary Amines (ChemCatChem 1/2021)

2020· article· en· W3126137416 on OpenAlexaff
Christopher J. Legacy, Taylor O. Hope, Yohann Gagné, Frederick T. Greenaway, Mathieu Frenette, Marion H. Emmert

Bibliographic record

VenueChemCatChem · 2020
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsChemistryCatalysisAmideOxidizing agentKinetic isotope effectIntramolecular forceLigand (biochemistry)Reaction mechanismPhotochemistryOrganic chemistryDeuterium

Abstract

fetched live from OpenAlex

The Cover Feature shows cast iron catalysis: Trialkyl amines cooked with the right recipe will take the oxygen from water to form the corresponding trialkyl amide. This transformation is iron-catalyzed with a peroxyester acting as the oxidizing agent, picolinate as a ligand and pyridine as the solvent. In their Full Paper, C. J. Legacy et al. present mechanistic studies including initial rate kinetics, Eyring studies, kinetic isotope effects, a Hammett plot, reaction kinetic profile, oxidant probe study, EPR characterization of the iron site and DFT studies. Taken together, the data suggests a rate limiting step where an intramolecular α-C−H abstraction occurs on the amine bound to an Fe(IV)-oxo species. Tasty! More information can be found in the Full Paper by C. J. Legacy et al.

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)0.001
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 score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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