3-Pyridinols and 5-pyrimidinols: Tailor-made for use in synergistic radical-trapping co-antioxidant systems
Bibliographic record
Abstract
The incorporation of nitrogen atoms into the aromatic ring of phenolic compounds has enabled the development of some of the most potent radical-trapping antioxidants ever reported. These compounds, 3-pyridinols and 5-pyrimidinols, have stronger O-H bonds than equivalently substituted phenols, but possess similar reactivities toward autoxidation chain-carrying peroxyl radicals. These attributes suggest that 3-pyridinols and 5-pyrimidinols will be particularly effectiveco-antioxidants when used in combination with more common, but less reactive, phenolic antioxidants such as 2,6-di-tert-butyl-4-methylphenol (BHT), which we demonstrate herein. The antioxidants function in a synergistic manner to inhibit autoxidation; taking advantage of the higher reactivity of the 3-pyridinols/5-pyrimidinols to trap peroxyl radicals and using the less reactive phenols to regenerate them from their corresponding aryloxyl radicals. The present investigations were carried out in chlorobenzene and acetonitrile in order to provide some insight into the medium dependence of the synergism and the results, considered with some from our earlier work, prompt a revision of the H-bonding basicity value of acetonitrile to β2 (H) of 0.39. Overall, the thermodynamic and kinetic data presented here enable the design of co-antioxidant systems comprising lower loadings of the more expensive 3-pyridinol/5-pyrimidinol antioxidants and higher loadings of the less expensive phenolic antioxidants, but which are equally efficacious as the 3-pyridinol/5-pyrimidinol antioxidants alone at higher loadings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".