Impact of Ionomer Content on Proton Exchange Membrane Fuel Cell Performance
Bibliographic record
Abstract
Abstract The effect of Nafion ionomer content on performance of a proton exchange membrane (PEM) fuel cell operated with home‐made anodic and cathodic electrodes fabricated from a novel metal organic framework (MOF) derived Pt‐based electrocatalyst was investigated via numerical simulation and experimental measurement. First, the parameter sensitivity analysis was performed to identify the most influential parameters of the model. Then, these parameters were calibrated for different fuel cell designs investigated in the current study by employing the corresponding experimental data. Afterwards, the calibrated model was used to examine the impact of Nafion content in the catalyst layer of home‐made electrodes. Finally, the qualitative trend predicted by this model was experimentally surveyed by varying the Nafion content between 10–50 wt.% in the catalyst layer of home‐made electrodes. At the anode side, the performance of home‐made electrode in a PEM fuel cell demonstrated small dependency on Nafion content. For the cathodic home‐made electrode, Nafion content was found to affect the PEM fuel cell performance more strongly. Although the model could correctly capture the impact of Nafion content on calculated polarization curves, the model predicted optimum values significantly deviate from the experimental results. This was related to the several simplifications made during model development.
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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 | 0.001 | 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".