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Record W2043587082 · doi:10.1002/jcc.21124

Stability of carbon‐centered radicals: Effect of functional groups on the energetics of addition of molecular oxygen

2008· article· en· W2043587082 on OpenAlexaff
James S. Wright, Hooman Shadnia, Leonid Chepelev

Bibliographic record

VenueJournal of Computational Chemistry · 2008
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsCarleton University
Fundersnot available
KeywordsRadicalBond-dissociation energyChemistryDissociation (chemistry)OxygenDensity functional theoryComputational chemistryUnpaired electronCarbon fibersPhysical chemistryChemical physicsMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper we examine a series of hydrocarbons with structural features which cause a weakening of the C-H bond. We use theoretical calculations to explore whether the carbon-centered radicals R(*) which are created after breaking the bond can be stabilized enough so that they resist the addition of molecular oxygen, i.e. where the reaction R(*) + O(2) --> ROO(*) becomes energetically unfavorable. Calculations using a B3LYP-based method provide accurate bond dissociation enthalpies (BDEs) for R-H and R-OO(*) bonds, as well as Gibbs free energy changes for the addition reaction. The data show strong correlations between R-OO(*) and R-H BDEs for a wide variety of structures. They also show an equally strong correlation between the R-OO(*) BDE and the unpaired spin density at the site of addition. Using these data we examine the major functional group categories proposed in several experimental studies, and assess their relative importance. Finally, we combine effects to try to optimize resistance to the addition of molecular oxygen, an important factor in designing carbon-based antioxidants.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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

Citations135
Published2008
Admission routes1
Has abstractyes

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