Measurement of Mechanical Behavior of Pristine Fuel Cell Electrodes Using Water Surface
Bibliographic record
Abstract
Mechanical robustness of polymer electrolyte fuel cell (PEFC) electrodes is essential to keep the performance and to improve the durability. To increase the mechanical robustness, mechanical behavior of PEFC electrodes should be investigated because the mechanical failures such as cracks, tears and punctures are critical to lifetime decrease. However, there are no attempts to investigate the mechanical behavior of a pristine electrode without any substrates because it has been a significant challenge due to its porous and brittle nature. Therefore, we present a novel method that can be used to investigate the mechanical behavior of free-standing PEFC electrodes on the water surface. The water surface enables measurement of the mechanical properties of electrodes due to its high surface tension and low viscosity. To separate the pristine electrode from the decal transfer film without damage, we adopted an innovative ice-assisted separation method. The tensile tests on water surface were conducted to understand of the effect of the ionomer content and Pt loading. Young’s modulus, elongation at break and tensile stress at break increased with increasing ionomer content in all Pt loading conditions. Also, the scaling law relationship of E~ρ1.6 between Young’s modulus and density was suggested. Our method can be used to design mechanically robust electrode for PEFC or evaluate the degradation degree of electrodes.
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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) | 0.001 | 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".