In Situ Control of Phenol Adsorption on Conductive Pd−Fluorine-Doped Tin Dioxide-Supported and Pd−Alumina-Supported Catalysts in Electrocatalytic Hydrogenation
Bibliographic record
Abstract
In the context of the electrocatalytic hydrogenation (ECH) process of unsaturated organic molecules, we have shown using infrared spectroscopy and water contact angle measurements that catalysts powders made of palladium on conductive tin dioxide (10% Pd/SnO2:F) and on alumina (10% Pd/Al2O3) are functionalized with organic chains when they were dipped in supporting electrolyte aqueous solutions containing different carboxylic acids. The carboxylic acids are bound to the supports (SnO2:F and Al2O3) through either the carboxyl or carboxylate groups. The measurement of contact angles confirmed that the support surface is functionalized by the carboxylic acids but also indicated the hydrophobic or hydrophilic character of the resultant surface. With these functionalized catalysts, the effectiveness of electrocatalytic hydrogenation of phenol could be modulated by controlling the adsorption of phenol. The adsorption depends mainly on the functionalization agent (carboxylic acid) and to a lesser extent on the identity of the support material (SnO2:F or Al2O3). Because adsorption is the step that induces the selectivity of the ECH process, controlling this phenomenon by functionalizing the catalyst support in situ is promising for obtaining molecules of choice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".