Electrochemical Reduction of Nitrate on Pyrolytic Graphite-Supported Cu and Pd–Cu Electrocatalysts
Bibliographic record
Abstract
The electrochemical reduction of nitrate and nitrite on Cu- and Pd–Cu-modified pyrolytic graphite electrodes was studied in neutral and alkaline media. The beneficial effect of Pd on the intrinsic electrocatalytic performance of the modified graphite electrode was demonstrated. In fact, the presence of Pd on the catalytic surface causes a positive potential shift in the onset of nitrate reduction current and a remarkable increase of the faradaic current. The activation energy of the nitrate electroreduction was estimated for the Cu/graphite and Pd–Cu/graphite electrodes at about 32 and , respectively. The reaction rate constants were equal to and for the Cu and Pd–Cu surface, respectively, reflecting a more facile electron-transfer process on the latter surface. The beneficial effect of Pd on the catalytic stability is probably related to the existence of a protective Pd layer enveloping the Cu core in the electrode surface structure. The selectivity of the modified electrodes depends on the electrolysis time and potential. In neutral medium, the nitrate electroreduction mainly led to the formation of nitrite at and ammonia at vs Ag/AgCl. The reaction selectivity was also studied in alkaline medium at for the Pd–Cu/graphite electrode only. In this case, the maximal selectivity for the production of was 70% and was achieved with a surface composition of 95 atom % Pd–5 atom % Cu.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".