Dual-Beam FIB/SEM Characterization, Statistical Reconstruction, and Pore Scale Modeling of a PEMFC Catalyst Layer
Bibliographic record
Abstract
The three-dimensional structure of a PEMFC catalyst layer (CL) was obtained using a dual beam Focused Ion Beam/Scanning Electron Microscope (FIB/SEM). Lower order statistical functions such as porosity and two point correlation functions calculated from the FIB/SEM data were used in the numerical reconstruction of a multi-phase CL domain. A 'carbon-sphere-based' initial seed structure, when optimized by simulated annealing, produced a structure for which the two point correlation function matched with the FIB/SEM data with very high fidelity. The reconstructed CL domain with its phase-resolved nano-structure was used to perform numerical simulation and predict effective transport properties. The coupled partial differential equations governing the reactive transport of charged and neutral species in the CL were discretized based on the finite volume method and solved implicitly using parallel computing. The simulated values of the effective oxygen and water vapor diffusivity, proton and electron conductivity, and thermal conductivity were in reasonable agreement with measured data reported in the literature. A parametric study was performed to investigate the impact of inaccessible pores–a feature that is not resolved experimentally. The simulations indicate that the properties most sensitive to the presence of inaccessible pores are the effective O 2 and H 2 O diffusivities and the effective proton conductivity.
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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 | 0.000 |
| 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 | 0.001 | 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".