Optimized Surface Strain in L1<sub>0</sub>-Type Pt<sub>0.8</sub>Ga<sub>0.2</sub>Co Intermetallic Catalyst for Enhanced Oxygen Reduction in Fuel Cells
Bibliographic record
Abstract
Tuning surface strain has been proven to be an efficient strategy for improving the kinetics of the oxygen reduction reaction of Pt–M electrocatalysts (M = non-noble metals). However, it remains a grand challenge to achieve optimal compressive strain, particularly on a platform of low-Pt nanocrystals. Herein, we report a novel approach involving the partial substitution of a Pt site with Ga, resulting in the development of a high-performance L1 0 -type Pt 0.8 Ga 0.2 Co intermetallic catalyst. The incorporation of Ga not only fine-tunes the surface strain to approach the optimum region of the theoretical volcano plot but also facilitates the formation of a more stable intermetallic structure dynamically. This enhancement significantly improves long-term electrochemical durability. Pt 0.8 Ga 0.2 Co/C exhibits a markedly improved intrinsic activity of 3.39 mA cm –2 and, more importantly, a high mass activity of 0.77 A mg Pt –1 at 0.90 V in a fuel cell, surpassing the performance of most previously reported L1 0 Pt-based intermetallics. Notably, catalytic durability is confirmed through only 28% mass activity loss after 30,000 potential cycles (vs 40% loss for the DOE target). This work paves the way for the development of promising low-Pt electrocatalysts for efficient energy conversion devices.
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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".