Bibliographic record
Abstract
Abstract Antioxidants are omnipresent in nature and industry; they are used to slow down the autoxidative degradation of both organic tissues and petroleum‐derived products. Various mechanisms by which antioxidants can act are known, the most common of which are introduced here. Since autoxidation is a radical chain reaction, radical‐trapping antioxidants play a particularly important role; therefore, we will discuss these compounds in most detail. To introduce and develop central concepts relating to this topic, we present kinetic and thermodynamic data obtained for the reactions of phenols, the archetypical radical‐trapping antioxidants, with chain‐carrying peroxyl radicals, and describe the means to obtain these data. We then extend our discussion to other important classes of radical‐trapping antioxidants, beginning with the closely related polyphenols and newly developed pyridinol and pyrimidinol antioxidants, and then further to aromatic amines, organosulfur compounds, and their derivatives. We go on to touch upon the synergistic behavior in antioxidant combinations, and the effects of the medium on these interactions, and finally round things up with a few more important biological examples: ascorbate and β‐carotene. A few comments on the state of the field and areas requiring further research and development are included.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".