Lattice-distorted Pt wrinkled nanoparticles for highly effective hydrogen electrocatalysis
Bibliographic record
Abstract
Modulating Pt surfaces through the introduction of lattice distortion emerges as immensely effective strategy that enhances the kinetics of alkaline hydrogen evolution and oxidation processes. In this study, we fabricated lattice-distorted Pt wrinkled nanoparticles (LD-Pt WNPs) for efficient hydrogen electrocatalysis. The LD-Pt WNPs not only outperform the Pt/C benchmark in hydrogen oxidation reaction, achieving an excellent mass-specific current of 968.5 mA·mg Pt −1 (9 times that of Pt/C), but also demonstrate outstanding hydrogen evolution reaction activity with a small overpotential of 58.0 mV. Comprehensive experiments and density functional theory calculations reveal that lattice defects introduce an abundance of unsaturated coordination atoms while modifying the d-band center of Pt. This dual effect optimizes the binding strength of crucial H and OH intermediates, leading to a significant reduction in the energy barrier of the reaction bottleneck, commonly known as the Volmer step. This work unveils a fresh viewpoint on projecting and developing high efficiency electrocatalysts through defect engineering.
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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".