Analysis of Catalyst Layer Microstructures: From Imaging to Performance
Bibliographic record
Abstract
Abstract Image analysis and numerical simulation algorithms are introduced to analyze the micro‐structure, transport, and electrochemical performance of thin, low platinum loading inkjet printed electrodes. A local thresholding algorithm is used to extract the catalyst layer pore morphology from focused ion beam scanning electron microscopy (FIB‐SEM) images. n‐point correlation functions, such as auto‐correlation, chord length, and pore‐size distribution are computed to interpret the micro‐structure variations between different images of the same catalyst layer. Pore size distributions are in agreement with experimental results. The catalyst layer exhibits anisotropy in the through‐plane direction, and artificial anisotropy in the FIB direction due to low slicing resolution. Microscale numerical mass transport simulations show that transport predictions are affected by image resolution and that a minimum domain size of 200 nm is needed to estimate transport properties. A micro‐scale electrochemical model that includes a description of the ionomer film resistance and a multi‐step electrochemical reaction model for the oxygen reduction reaction is also presented. Results show that the interfacial mass transport resistance in the ionomer film has the largest effect on the electrochemical performance.
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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) | 0.001 | 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".