(Invited) Measuring and Modeling Transport Processes in Porous Electrodes
Bibliographic record
Abstract
The porous materials found inside fuel cells and other electrochemical devices differ substantially from the traditional porous media studied by geologists and reservoir engineers (i.e. rock, sand and soil). The list of challenging features of porous electrode materials is long: they can be extremely thin, nanoporous, electrically conductive, anisotropic, highly porous, fibrous, multilayered, fractured, compressible, and not self-supporting. As such, the standard set of experimental tools and modeling techniques are not always suitable or applicable. In this tutorial, progress made in porous materials characterization in the fuel cell field over the past decade will be summarized, but this tutorial will be of interest to workers in any application involving engineered porous materials. Specific topics will include pore phase measurements of effective diffusivity and permeability, solid phase measurements such as electronic and thermal conduction, multiphase flow related processes such as capillary pressure curves and breakthrough conditions, as well as pore space characterization techniques such as porosity, pore size distributions, and X-ray tomography. The role and value of the various transport parameters will also be discussed in relation to modeling and simulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".