How good are the Electrodes we use in PEFC?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it