Effect of Water Sorption on the Electronic Conductivity of Porous Polymer Electrolyte Membrane Fuel Cell Catalyst Layers
Bibliographic record
Abstract
A method is described for measuring the effective electronic conductivity of porous fuel cell catalyst layers (CLs) as a function of relative humidity (RH). Four formulations of CLs with different carbon black (CB) contents and ionomer equivalent weights (EWs) were tested. The van der Pauw method was used to measure the sheet resistance (RS), which increased with RH for all samples. The increase was attributed to ionomer swelling upon water uptake, which affects the connectivity of CB aggregates. Greater increases in RS were observed for samples with lower EW, which uptake more water on a mass basis per mass ionomer. Transient RS measurements were taken during absorption and desorption, and the resistance kinetics were fit using a double exponential decay model. No hysteresis was observed, and the absorption and desorption kinetics were virtually symmetric. Thickness measurements were attempted at different RHs, but no discernible changes were observed. This finding led to the conclusion that the conducting Pt/C volume fraction does not change with RH, which suggests that effective medium theory models that depend on volume fraction alone cannot explain the reduction in conductivity with RH. The merits of percolation-based models were discussed. Optical micrographs revealed an extensive network of "mud cracks" in some samples. The influence of water sorption on CL conductivity is primarily explained by ionomer swelling, and its effects on the quantity and quality of interaggregate contacts were discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".