Strain-Modulated Platinum–Palladium Nanowires for Oxygen Reduction Reaction
Bibliographic record
Abstract
Electrocatalytic activity of alloy nanocatalytsts can be manipulated effectively by tuning their physical properties (ensemble, geometric, and ligand effects) to afford optimal surface structure and compositions for proton exchange membrane fuel cell (PEMFC) application. Herein, highly catalytic platinum–palladium nanowires (Pt n Pd 100– n NWs) with a subtle lattice strain and Boerdijk–Coxeter helix type morphology are synthesized through a surfactant-free, thermal single phase solvent method. X-ray diffraction results show that Pt n Pd 100– n NWs are exposed through the (111) facets and their shrinking or expanding lattice parameters can be modulated by the alloy compositions. Electrochemical results reveal that their high catalytic activity correlates with the lattice shrinking, facets, and bimetallic compositions, showing higher activity when the ratio of Pt and Pd is ∼78:22, which is further supported by DFT results. Compared to the nanoparticle type platinum–palladium alloyed catalysts with similar metal compositions (Pt n Pd 100– n NPs), the Pt n Pd 100– n NWs exhibit significantly improved electrocatalytic activity and stability for the oxygen reduction reaction. These findings open new strategies to design the highly active and stable alloy nanocatalysts with controllable compositions.
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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".