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Record W4385268344 · doi:10.1139/cjc-2022-0089

Oxidation of secondary hydroperoxides via αC–H abstraction to form ketones and hydroxyl radicals: fluorene autoxidation as a model system

2023· article· en· W4385268344 on OpenAlexafffundvenue
Mélanie Sollin, Seyedehsan Hosseininasab, Jason Malenfant, Mohamed El-Akhrass, Amaia López de Arbina, Alicia Montulet, Mathieu Frenette

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutoxidationChemistryRadicalPhotochemistryKetoneSolventOrganic chemistry

Abstract

fetched live from OpenAlex

Organic and biological molecules with relatively weak C–H bonds can react with O 2 via a free-radical reaction called autoxidation. The primary products of these peroxyl-radical-driven reactions are hydroperoxides, R 2 CHOOH. If autoxidation continues, the secondary oxidation of hydroperoxides is known to form ketones, R 2 C═O, but this mechanism is not well characterized. Importantly, we find that ketone formation produces a highly reactive hydroxyl radical, HO • . We can trap HO • using benzene as a solvent to form quantifiable amounts of phenol. Fluorene was chosen as a model system to study this secondary oxidation in great detail. Kinetic modeling allowed the measurement of rate constants for the primary and secondary autoxidation reactions as 11.3 and 25 mol L −1 s −1 , respectively. Density functional theory modeling likewise predicts a faster oxidation for the secondary autoxidation. This type of kinetic measurement and modeling approach could be useful to study the autoxidation of plastics, petrochemicals, and lipids.

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 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 score0.014
Threshold uncertainty score0.748

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 science0.0000.000
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 head0.219
Teacher spread0.209 · 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.

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

Citations1
Published2023
Admission routes3
Has abstractyes

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