How good are the Electrodes we use in PEFC?
Bibliographic record
Abstract
Abstract Basically, companies and laboratories implement production methods for their electrodes on the basis of experience, technical capabilities and commercial preferences. But how does one know whether they have ended up with the best possible electrode for the components used? What should be the (i) optimal thickness of the catalyst layer? (ii) relative amounts of electronically conducting component (catalyst, with support – if used), electrolyte and pores? (iii) “particle size distributions” in these mesophases? We may be pleased with our MEAs, but could we make them better? The details of excellently working MEA structures are typically not a subject of open discussion, also hardly anyone in the fuel cell business would like to admit that their electrodes could have been made much better. Therefore, we only rarely find (far from systematic) experimental reports on this most important issue. The message of this paper is to illustrate how strongly the MEA morphology could affect the performance and to pave the way for the development of the theory. Full analysis should address the performance at different current densities, which is possible and is partially shown in this paper, but vital trends can be demonstrated on the linear polarization resistance, the signature of electrode performance. The latter is expressed through the minimum number of key parameters characterizing the processes taking place in the MEA. Model expressions of the percolation theory can then be used to approximate the dependence on these parameters. The effects revealed are dramatic. Of course, the corresponding curves will not be reproduced literally in experiments, since these illustrations use crude expressions inspired by the theory of percolation on a regular lattice, whereas the actual mesoscopic architecture of MEA is much more complicated. However, they give us a flavour of reserves that might be released by smart MEA design.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".