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Record W2023476524 · doi:10.1002/poc.763

Reactions of carbocations with water and azide ion: calculation of rate constants from equilibrium constants and distortion energies using No Barrier Theory

2004· article· en· W2023476524 on OpenAlexafffund
J. Peter Guthrie, Vladimir Pitchko

Bibliographic record

VenueJournal of Physical Organic Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicChemical Reaction Mechanisms
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryReaction rate constantAzideEquilibrium constantCarbocationSolvationPhysical chemistryComputational chemistryThermodynamicsIonPhotochemistryOrganic chemistryKinetics

Abstract

fetched live from OpenAlex

Abstract Rate constants for the reaction of water or azide ion with various substituted benzylic carbocations can be calculated from the equilibrium constants for cation formation and distortion energies by means of the No Barrier Theory. Rate constants for the water reactions span 10 orders of magnitude. Most but not all of the azide reactions are diffusion controlled. The rate constants, and in particular those less than diffusion controlled, were successfully calculated. The set of equilibrium constants available from the literature was supplemented using alkyl chloride hydrolysis equilibrium constants and relative cation formation equilibrium constants derived from DFT/continuum calculations. The model used for these reactions allows for the entropic cost of bringing solutes together, the desolvational cost of losing hydrogen bonding solvation by water and the cost of moving the aryl ring through the solvent as the central atom changes from sp2 to sp3 hybridization and from planar to pyramidal geometry. Copyright © 2004 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.224
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations20
Published2004
Admission routes2
Has abstractyes

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