(Digital Presentation) Electrocatalytic Transformations of Phenolic Compounds
Bibliographic record
Abstract
Electrocatalytic transformations giving rise to new C-C bond formation and new products are of value. We have recently discovered that phenolic compounds undergo selective electrochemical oxidation giving rise to reactive radicals which combine to produce product with new C-C bond formed [1]. A series of phenolic compounds, including butylated hydroxytoluene (BHT), 4-tert-butylphenol (4TBP), 2-tert-butylphenol (2TBP), 2,4,6-tri-tert-butylphenol (TTBP), 2,6,-di-tert-butylphenol (DTBP), diphenylphenol (DPP), and trichlosan were systematically evaluated by electrochemical methods.The selective electrochromic behavior was observed via electrochemical cycling. The electrocatalytic oxidation of substrates resulted in a formation of new compounds due to C-C coupling, or related products. The products were characterized by X-ray diffraction, GC-MS, and NMR, among other methods. The electrochemical synthesis was compared to the traditional chemical synthesis and the reaction yields and products were highly dependent on the synthetic strategy used and a substrate. References Zabik, N., Virca, C. N., McCormick, T., Martic-Milne, S. (2016). Selective electrochemical versus chemical oxidation of bulky phenols. J. Phys. Chem. B. 120: 8914-8924.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.533 | 0.299 |
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".