On the efficiency of phenol and cyclohexanone electrocatalytic hydrogenation — Effect of conditioning and working pH in acetic acid solution on palladium/fluorine-doped tin dioxide supported catalyst
Bibliographic record
Abstract
The electrocatalytic hydrogenation (ECH) of phenol and cyclohexanone was performed on a conductive Pd/SnO 2 :F catalyst. The catalyst was obtained by the impregnation method. We studied the influence of the pH of the supporting electrolyte, the conditioning pH, and the quantity of the conditioning charge passed before hydrogenation. Fourier transform infrared spectroscopy analysis showed that the functionalization of the catalyst surface by the acetic acid electrolyte depends on the pH. A direct correlation was observed between the efficiency of the hydrogenation, the pH of the electrolyte, and the electrode conditioning charge. Phenol hydrogenation was favored in acidic media, whereas cyclohexanone hydrogenation needed an acidic medium for conditioning and a basic medium for hydrogenation. The ECH rate appeared to depend on the functionalization of the catalyst surface, the adsorption of the target organic molecule on the catalyst, and its structural modification with the pH.
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 | 0.000 | 0.001 |
| 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".