Three-Dimensional Anisotropic Electrical Resistivity of PEM Fuel Cell Transport Layers as Functions of Compressive Strain
Bibliographic record
Abstract
We report on a method and apparatus for the determination of the three-dimensional electronic resistance of proton-exchange membrane fuel cell porous transport layers (PTLs) as a function of compression. Attention is given to the anisotropic nature of the fiber-based materials; between through- and in-plane directions and the differences within in-plane. For the first time, in-plane resistivities are resolved as functions of strain with a square four-point probe (4PP) arrangement; these are acquired in multiple directions to determine principal components of resistivity. Through-plane information is acquired with a linear 4PP apparatus with parallel capture of sample stress and strain. Results are presented for a selection of PTLs of different PTFE contents and thicknesses. In-plane resistivities exhibit linear decreases in resistivity with increasing compressive strains. We estimate an order of magnitude lower resistivity in- versus through-plane. Further, we reinforce that anisotropy in-plane undermines reports based on two or less measurements. Three methods are presented and contrasted for analyzing data in the through-plane direction. We demonstrate the limitations of common assumptions regarding contact resistances and of using PTLs of different thicknesses. It is proposed a total area resistance approach is presently the most accessible and communicable for the fuel cell community.
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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 | 0.000 |
| 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 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".