Modelling of Proton Density Distribution in Active Nanopores of Fuel Cell Catalyst Layers
Bibliographic record
Abstract
Proton density and mobility in porous electrodes determine the proton conductivity as well as the rates of interfacial electrochemical processes. In the case of polymer electrolyte fuel cells (PEFCs), the foremost practical objective is to design porous electrodes or catalyst layers with high performance at markedly reduced platinum loading. Achieving this objective demands an understanding of the impact of composition and porous structure as well as surface structure and charging properties of pore walls on the proton density distribution 1 . In the presented work, we consider simple pore geometries to study the concerted effects of ionomer structure and metal charging properties on proton density in nanopores. The approach employs Poisson-Nernst-Planck theory 2, 3 . The basic model system is a cylindrical pore confined by an ionomer shell. The core to consist of a solid metal rod and the gap space between the core and the ionomer shell is filled with water. The set of ordinary differential equations for proton density and reaction in these model structures is formulated and solved. Solutions are analysed by comparing potential and proton density distributions for varying pore geometries and charging properties at interfaces. The effectiveness factor of catalyst utilization is calculated to evaluate the electrocatalytic performance at the pore level 4, 5 . References [1] A.Weber et al., J. Electrochem. Soc. 161(12), (2014), F1254-F1299 [2] R.Coalson and M.Kurnikova, Biological membrane Ion channels, Chap 13, Springer, NewYork (2001) [3] K.Chan and M.Eikerling, J. Electrochem. Soc. 158(1), (2011), B18-B28. [4] K.Chan and M.Eikerling, J. Electrochem. Soc. 159(2), (2012), B155-B164 [5] A.Bonnefont, F.Argoul, and M.Z.Bazant, J. Electroanal. Chem., 500, 52, (2011
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".