Dilute Pd−Ni Alloy through Low‐temperature Pyrolysis for Enhanced Electrocatalytic Hydrogen Oxidation
Bibliographic record
Abstract
Abstract Designing highly active and cost‐effective electrocatalysts for the alkaline hydrogen oxidation reaction (HOR) is critical for advancing anion‐exchange membrane fuel cells (AEMFCs). While dilute metal alloys have demonstrated substantial potential in enhancing alkaline HOR performance, there has been limited exploration in terms of rational design, controllable synthesis, and mechanism study. Herein, we developed a series of dilute Pd−Ni alloys, denoted as x% Pd−Ni, based on a trace‐Pd decorated Ni‐based coordination polymer through a facile low‐temperature pyrolysis approach. The x% Pd−Ni alloys exhibit efficient electrocatalytic activity for HOR in alkaline media. Notably, the optimal 0.5 % Pd−Ni catalyst demonstrates high intrinsic activity with an exchange current density of 0.055 mA cm−2, surpassing that of many other alkaline HOR catalysts. The mechanism study reveals that the strong synergy between Pd single atoms (SAs)/Pd dimer and Ni substrate can modulate the binding strength of proton (H)/hydroxyl (OH), thereby significantly reducing the activation energy barrier of a decisive reaction step. This work offers new insights into designing advanced dilute metal or single‐atom‐alloys (SAAs) for alkaline HOR and potentially other energy conversion processes.
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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) | 0.000 | 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".