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Record W2313742745 · doi:10.1021/jo302483s

Reactions of the Phthalimide<i>N</i>-Oxyl Radical (PINO) with Activated Phenols: The Contribution of π-Stacking Interactions to Hydrogen Atom Transfer Rates

2013· article· en· W2313742745 on OpenAlexaff
Claudio D’Alfonso, Massimo Bietti, Gino A. DiLabio, Osvaldo Lanzalunga, Michela Salamone

Bibliographic record

VenueThe Journal of Organic Chemistry · 2013
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersCentre National de la Recherche Scientifique
KeywordsChemistryPhthalimideHydrogen atomPhenolsStackingAtom (system on chip)HydrogenStereochemistryOrganic chemistryGroup (periodic table)

Abstract

fetched live from OpenAlex

The kinetics of reactions of the phthalimide N-oxyl radical (PINO) with a series of activated phenols (2,2,5,7,8-pentamethylchroman-6-ol (PMC), 2,6-dimethyl- and 2,6-di-tert-butyl-4-substituted phenols) were investigated by laser flash photolysis in CH(3)CN and PhCl in order to establish if the reactions with PINO can provide a useful tool for evaluating the radical scavenging ability of phenolic antioxidants. On the basis of the small values of deuterium kinetic isotope effects, the relatively high and negative ρ values in the Hammett correlations and the results of theoretical calculations, we suggest that these reactions proceed by a hydrogen atom transfer (HAT) mechanism having a significant degree of charge transfer resulting from a π-stacked conformation between PINO and the aromatic ring of the phenols. Kinetic solvent effects were analyzed in detail for the hydrogen transfer from 2,4,6-trimethylphenol to PINO and the data obtained are in accordance with the Snelgrove-Ingold equation for HAT. Experimental rate constants for the reactions of PINO with activated phenols are in accordance with those predicted by applying the Marcus cross relation.

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 categoriesInsufficient 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 score0.004
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.228
Teacher spread0.219 · 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

Citations30
Published2013
Admission routes1
Has abstractyes

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