Promoting Effect of Ni(OH)<sub>2</sub> on Palladium Nanocrystals Leads to Greatly Improved Operation Durability for Electrocatalytic Ethanol Oxidation in Alkaline Solution
Bibliographic record
Abstract
Most electrocatalysts for the ethanol oxidation reaction suffer from extremely limited operational durability and poor selectivity toward the CC bond cleavage. In spite of tremendous efforts over the past several decades, little progress has been made in this regard. This study reports the remarkable promoting effect of Ni(OH)2 on Pd nanocrystals for electrocatalytic ethanol oxidation reaction in alkaline solution. A hybrid electrocatalyst consisting of intimately mixed nanosized Pd particles, defective Ni(OH)2 nanoflakes, and a graphene support is prepared via a two‐step solution method. The optimal product exhibits a high mass‐specific peak current of >1500 mA mg−1Pd, and excellent operational durability forms both cycling and chronoamperometric measurements in alkaline solution. Most impressively, this hybrid catalyst retains a mass‐specific current of 440 mA mg−1 even after 20 000 s of chronoamperometric testing, and its original activity can be regenerated via simple cyclic voltammetry cycles in clean KOH. This great catalyst durability is understood based on both CO stripping and in situ attenuated total reflection infrared experiments suggesting that the presence of Ni(OH)2 alleviates the poisoning of Pd nanocrystals by carbonaceous intermediates. The incorporation of Ni(OH)2 also markedly shifts the reaction selectivity from the originally predominant C2 pathway toward the more desirable C1 pathway, even at room temperature.
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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 | 0.000 |
| 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".