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Record W2130522928 · doi:10.1255/ejms.355

The Effect of Phenyl Substitution on the Thermochemistry of Gas-Phase Ions and Their Neutral Counterparts

2000· article· en· W2130522928 on OpenAlexafffund
Christiane Aubry

Bibliographic record

VenueEuropean Journal of Mass Spectrometry · 2000
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermochemistrySubstitution (logic)ChemistryIonSubstitution reactionStandard enthalpy of formationElectron ionizationIonizationMedicinal chemistryGas phaseIonization energyCationic polymerizationCrystallographyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The available data for the heats of formation of phenyl-substituted organics have been critically evaluated. Phenyl substitution is always thermochemically destabilizing in that Δ f H 0 [R–C 6 H 5 ] – Δ f H 0 [R–H] is a positive quantity. The magnitude of the effect in neutrals depends on the substitution site, e.g. from + 79 kJ mol −1 for HCOR or HCCR to + 155 kJ mol −1 for RCO 2 H. Multiple phenyl substitution (where possible) at the same site is generally simply additive. For the corresponding cationic species, the first phenyl substitution is stabilizing when the ionization energy of the substrate is lowered thereby and for even-electron ions resonance-stabilization energies may also come into play. Additional phenyl substitution at the same site is, however, always destabilizing. Phenyl substitutions were compared with vinyl substitution: for neutral species the effects are similar, whereas for even-electron ions, multiple vinyl substitution is only weakly stabilizing or destabilizing.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.234
Teacher spread0.227 · 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 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

Citations4
Published2000
Admission routes2
Has abstractyes

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