Electrochemical Versus Chemical Oxidation of 2,6-Diphenylphenol
Bibliographic record
Abstract
The phenolic compounds are used in the industry, agriculture and biotechnology, and inevitably end up in our environment. These compounds may serve as a phenolic precursor to produce raw materials for a wide range of applications. The selective electrochemical oxidation of bulky phenols was recently achieved [1]. Herein, electrochemical oxidation of 2,6-diphenylphenol (DPP) was carried out and compared to traditional chemical oxidation. Contrasted with chemical oxidation, cyclic voltammetry resulted in a range of products based on the specific potential ranges used. The electrooxidation and chemical oxidation of DPP resulted in a solution colour change and the formation of new products monitored by UV-vis, and characterized by nuclear magnetic spectroscopy (NMR), X-ray single crystal diffraction, and gas chromatography-mass spectrometry (GC-MS). Our data indicate that the synthetic outcomes are dependent on the synthetic methodology employed, and that electrooxidation may yield products which are not always possible by chemical means. References [1] Zabik, N. L.; Virca, C. N.; McCormick, T. M.; Martic-Milne, S. Selective Electrochemical versus Chemical Oxidation of Bulky Phenol. J. Phys. Chem. B 2016, 120 (34), 8914–8924. https://doi.org/10.1021/acs.jpcb.6b06135. Figure 1
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".