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Record W1981246223 · doi:10.1021/ja0107529

Keto−Enol/Enolate Equilibria in the <i>N</i>-Acetylamino-<i>p</i>-methylacetophenone System. Effect of a β-Nitrogen Substituent

2001· article· en· W1981246223 on OpenAlexafffund
Y. Chiang, Axel G. Griesbeck, Heike Heckroth, Bruno Hellrung, A. J. Kresge, Qingshui Meng, AnnMarie C. O’Donoghue, John P. Richard, Jakob Wirz

Bibliographic record

VenueJournal of the American Chemical Society · 2001
Typearticle
Languageen
FieldChemistry
TopicChemical Reaction Mechanisms
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsChemistryKeto–enol tautomerismEnolSubstituentMethyleneMedicinal chemistryEquilibrium constantKetoneReaction rate constantPhotochemistryCatalysisInorganic chemistryTautomerOrganic chemistryKinetics

Abstract

fetched live from OpenAlex

The cis-enol of N-acetylamino-p-methylacetophenone was generated flash photolytically and its rates of ketonization in aqueous HClO(4) and NaOH solutions as well as in HCO(2)H, CH(3)CO(2)H, H(2)PO(4)(-), (CH(2)OH)(3)CNH(3)(+), and NH(4)(+) buffers were measured. Rates of enolization of N-acetylamino-p-methylacetophenone to the cis-enol were also measured by hydrogen exchange of its methylene protons, and combination of the enolization and ketonization data gave the keto-enol equilibrium constant pK(E) = 5.33, the acidity constant of the enol ionizing as an oxygen acid pQ(a)(E)= 9.12, and the acidity constant of the ketone ionizing as a carbon acid pQ(a)(K)= 14.45. Comparison of these results with corresponding values for p-methylacetophenone itself shows that the N-acetylamino substituent raises all three of these equilibrium constants: K(E) by 3 orders of magnitude, Q(a)(E) by 1 order of magnitude, and Q(a)(K)by 4 orders of magnitude. This substituent also retards the rate of H+ catalyzed enol ketonization by 4 orders of magnitude. The origins of these substituent effects are discussed.

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)0.001
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 score0.002
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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

Citations14
Published2001
Admission routes2
Has abstractyes

Explore more

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