Elucidating Cation Transport Properties in Nafion Membranes and Electrode Ionomer Network via X-ray Fluorescence Imaging
Bibliographic record
Abstract
Membranes and electrode ionomers in proton exchange membrane fuel cells are prone to cation contamination, leading to a reduction in performance. Despite the importance, the characteristics of cation mobility within membranes and ionomer thin films remain poorly understood. Here, we investigate Co2+ transport properties in membranes and electrode ionomers using synchrotron X-ray fluorescence imaging. Specifically, the samples are doped with a controlled Co2+ exchange and the samples are subsequently subject to hydrogen pump operation under fixed humidity and potential gradient. A 1-D model is developed based on the Nernst-Planck relation, which predicts the diffusion and mobility coefficients of Co2+. We also develop a characterization platform, termed Electrode Ionomer Network (EIN), for studying cation transport properties in electrode ionomers that are inherently tortuous and non-uniform. We observe that an increase in relatively humidity from 40 to 75% increases both the diffusion and mobility in Nafion membranes (by factors of 2.2 and 7.1, respectively), determined via fitting the Nernst-Planck relation to our experimental data. Despite the identical humidity conditions, Co2+ become less mobile in EINs relative to membrane (lower by 67% and 44% for diffusivity and mobility, respectively), which are attributed to confinement effects, and the tortuous and disconnected ionomer network in the electrode. Our results provide insights that can help predict cation concentration distributions across membrane-electrode assemblies for hydrogen fuel cell applications.
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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 | 0.001 |
| 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".