Bibliographic record
Abstract
Phenolic compounds are known for their antioxidant properties, which is important in the biological setting [1]. The cellular damage induced by reactive oxygen species, ROS, or others leads to diseases. However, foods or drugs which contain phenolic moieties are viable antioxidants which reduce ROS and minimize cell damage. Hence, greater understanding of this class of compounds will lead to an ideal antioxidant with tailored properties for applications in biology, food safety, among others. The relationship between structure-properties-function was evaluated by using voltammetric methods. We have explored redox properties of phenol-based compounds, such as tert-butylhydroxyphenol and its analogues by using Cyclic Voltammetry (CV) as well as Square-Wave Voltammetry (SWV) in order to rank order such compounds based on their redox potentials and structural parameters [2]. We have discovered that under electrochemical conditions, rather than chemical ones, such compounds may undergo cross-coupling reactions leading to photochromic products. The reactivity of such phenolic analogues was also explored by CV in the study with reactive oxygen species, specifically, superoxide anion radical [3]. More recently, we have started exploring compounds with multiple hydroxyl groups, such as flavonoids. The research findings on a variety of hydroxyl-containing aromatics will be presented and described. 1. Ingold, K. U., Pratt, D. A. Advances in radical-trapping antioxidant chemistry in the 21st century: a kinetic and mechanisms perspective. Chem. Rev. 2014, 9022-9046. 2. Zabik, N., Virca, C. N., McCormick, T., Martic-Milne, S. Selective electrochemical versus chemical oxidation of bulky phenols. J. Phys. Chem. B, 2016, 120, 8914–8924. 3. Zabik, N., Anwar, S., Ziu, I., Martic-Milne, S., Electrochemical reactivity of bulky-phenols with superoxide anion radical. Electrochim. Acta, 2019, 296, 174-180
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".