Revealing the roles of oxidation states and constituents of the alloy in alkaline hydrogen evolution reaction
Bibliographic record
Abstract
Precious metal Pt has exhibited excellent catalytic performance in numerous electrocatalytic applications. However, the slow water dissociation kinetics hinder its alkaline hydrogen evolution reaction (HER) activity. Herein, we design the partially oxidized PtRu alloys with fine-tuned structure which can significantly enhance the alkaline HER. Theoretical calculations reveal that the alteration of Pt/Ru ratio and surface oxidation state of PtRu materials can effectively tune the water and hydrogen adsorption capability, thus impacting the alkaline HER performance. We dentify Pt 70 Ru 30 with 3 O surface coverage with optimized adsorption energy to be the most suitable candidate for alkaline HER. Inspired by theoretical predictions, we prepare partially oxidized PtRu with tunable composition and structure via a facile urea-assisted method, with a “capture and release” reaction mechanism by generating complexing agents and pH gradients. The as-synthesized partially oxidized Pt 70 Ru 30 electrode exhibits excellent HER catalytic activity and stability in the alkaline electrolyte, achieving an overpotential of only 11 mV at 10 mA cm −2 and a Tafel slope of 28 mV dec −1 , outperforming that of Pt/C (η 10 = 29 mV, Tafel slope = 52 mV dec −1 ). In-situ spectral measurements show that the partially oxidized Pt and Ru species in Pt 7 0 Ru 3 0 can facilitate breaking the O-H bonds and improve the alkaline HER kinetics. This work unravels the influence of Pt/Ru ratio and surface partial oxidation on water dissociation and hydrogen adsorption, which provide guidelines for the rational design of highly efficient electrocatalysts. Guided by density functional theory calculations, partially oxidized Pt 70 Ru 30 was synthesized by urea decomposition-assisted method. The morphology and chemical composition of PtRu materials were rationally regulated by the "capture-release" mechanism. This work deepens the influence of Pt/Ru ratio and surface partial oxidation on water dissociation and hydrogen adsorption, providing guidance for the rational design of efficient electrocatalysts. • Altering Pt/Ru ratio and oxidation state boosts the alkaline HER performance. • Both partially oxidized Pt and Ru species in Pt 70 Ru 30 -3O serve as active sites for water adsorption and H* reactions. • PO-PtRu are synthesized by the "capture-release" mechanism induced by urea decomposition, exhibiting high HER activity. • In situ spectroscopy shows partially oxidized Pt / Ru oxidation enhances HO-H cleavage, boosting catalytic kinetics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".