Probing Electrode Structure Using Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
In this work, we extend the technique described by Lefebvre et al. to perform impedance measurements in N 2 /N 2 using Membrane Electrode Assemblies (MEAs) containing symmetrical electrodes at different relative humidities. The average ionic resistance of the individual catalyst layer can therefore be estimated from one half of the total average ionic resistance. The trends in the ionic resistance with RH can be extrapolated to obtain the value at 100% RH. The value at 100% RH is useful for benchmarking different electrode structures and compositions in terms of maximum possible ionic conductivity and for comparisons with fuel cell data obtained under stationary conditions wherein the RH is typically 100%. This paper discusses the details of the methodology adopted and provides examples wherein the use of impedance measurements allow us to gain valuable insight related to the composition and structure of these electrodes. For certain types of electrodes, or electrodes operating under sub-saturated conditions typical for automotive conditions, lower proton conductivity should have a significant negative effect on fuel cell performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".