Understanding the effect of porosity and pore size distribution on low loading catalyst layers
Bibliographic record
Abstract
Stochastic reconstructions, generated using an overlapping sphere algorithm with different particle sizes, were used to understand the role of the catalyst layer (CL) pore size distribution and porosity on the gas transport, local saturation and electrochemical performance of a low loading cathode. Statistical functions were used to characterize the morphology of the CLs and numerical simulations were performed to study the effective transport properties and electrochemical performance under dry and wet conditions. Results show that an increase in pore size increases the dry effective diffusivity but lowers the partially-saturated diffusivity at a given capillary pressure due to higher local saturation in the CL. Under dry conditions, porosity and particle size had negligible effect on the electrochemical performance of low loading CLs despite substantial changes in the ionomer distribution. Electrochemical simulation results at different liquid pressures show that CLs with moderate porosity and small particle size would maximize performance at a given capillary pressure due to lower liquid water accumulation, higher evaporation driven water transport and lower probability of water breakthrough to the diffusion media.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".