The effect of non-spherical platinum nanoparticle sizes on the performance and durability of proton exchange membrane fuel cells
Bibliographic record
Abstract
Platinum (Pt) nanoparticles with different sizes of 2 nm and 5 nm supported on functionalized high surface area carbon (HSC) have been successfully synthesized with a one-pot synthesis technique in large scale. Of the interest for the proton exchange membrane fuel cell applications, the synthesized supported catalysts are evaluated by physical characterizations, half-cell and scaled up single cell tests to study the impact of the catalyst sizes on cell performance and durability. Physical characterizations clearly demonstrate the sizes, shapes, crystallinity phases, and the total loading of the Pt nanoparticles on HSC. Half cell characterizations demonstrate higher electrochemical surface area, higher mass activity, and less durability for the working electrode prepared by the smaller Pt nanoparticle sizes (2 nm) than the larger Pt nanoparticles (5 nm). Scaled up single cell tests using air and hydrogen as the cathode and anode reactants demonstrate the membrane electrode assembly (MEA) prepared by smaller Pt nanoparticle sizes (2 nm) shows the maximum power density of 1.1 W/cm 2 , which is 7% higher than the maximum power density of MEA prepared by larger Pt nanoparticles (5 nm) under similar operational conditions. The 30,000 cycles of accelerated stress test on the membrane electrode assembly prepared by larger Pt nanoparticles (5 nm) demonstrates 13% drop at maximum power density, illustrating the excellent performance against degradation (ageing).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".