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Record W2581875090 · doi:10.1002/cjce.22798

Kinetic studies on the liquid‐phase catalytic oxidation of 4‐methyl guaiacol to vanillin

2017· article· en· W2581875090 on OpenAlexvenueno aff
Qiyi Ma, Kejing Liu, Jianyong Mao, Kexian Chen, Liang Cheng, Jia Yao, Zhirong Chen, Haoran Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsGuaiacolChemistryCatalysisReaction mechanismVanillinPyridiniumKineticsKinetic resolutionEtherCobaltMass transferPhotochemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

Kinetic studies on the catalytic oxidation of 4‐methyl guaiacol using an ether intermediate towards vanillin in an agitator bubbling reactor were presented. The reaction was catalyzed by N,N′ ‐ethylenebis(acetylacetoniminato)‐cobalt(II) hexafluorophosphoric pyridinium (Co‐[Salen‐Py][PF 6 ] 2 ) under strong alkaline conditions with molecular oxygen as the terminal oxidant. The reaction scheme was demonstrated by a detailed analysis of the products distribution and control experiments. The intrinsic kinetics were explored based on our proposed p ‐benzoquinone methide mechanism, which revealed that the oxidation rate was determined by the oxidation of phenoxyl anion to phenoxyl radical catalyzed by LCo 3+ (O 2 ·) (L = [Salen‐Py][PF 6 ] 2 ). The kinetic experiments were performed under a mass transfer‐reaction condition due to the inevitable mass transfer resistance involved. Pseudo‐first order fast reactions were observed for both steps of the consecutive reactions based on the calculated physical and mass transfer parameters. The intrinsic kinetic equations for the first and second steps can be written as and , respectively, which are well in accordance with the mechanism. These conclusions are expected to give a clue for the mechanistic study, kinetic investigation, reaction optimization, and catalyst design for the oxidation of various p ‐cresols.

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)0.004
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 score0.003
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 integrity0.0000.000
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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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