On the Micro-, Meso-, and Macroporous Structures of Polymer Electrolyte Membrane Fuel Cell Catalyst Layers
Bibliographic record
Abstract
In this work, N(2) adsorption was employed to investigate the effects of carbon support, platinum, and ionomer loading on the microstructure of polymer electrolyte membrane fuel cell catalyst layers (CLs). Brunauer-Emmett-Teller and t-plot analyses of adsorption isotherms and pore-size distributions were used to study the microstructure of carbon supports, platinum/carbon catalyst powders, and three-component platinum/carbon/ionomer CLs. Two types of carbon supports were chosen for the investigation: Ketjen Black and Vulcan XC-72. CLs with a range of Nafion ionomer loadings were studied in order to evaluate the effect of an ionomer on the CL microstructure. Regions of adsorption were differentiated into micropores associated with the carbon primary particles (<2 nm), mesopores ascribed to the void space inside agglomerates (2-20 nm), and meso- to macroporous space inside aggregates of agglomerates (>50 nm). Ketjen Black was found to possess a significant fraction of micropores, 25% of the total pore volume, in contrast to Vulcan XC-72, for which the corresponding fraction of micropores was 15% of the total pore volume. The microstructure of the carbon support was found to be a significant factor in the formation of the microstructure in the three-component CLs, serving as a rigid porous framework for distribution of platinum and the ionomer. It was found that platinum particle deposition on Ketjen Black occurs in, or at the mouth of, the support's micropores, thus affecting its effective microporosity, whereas platinum deposition on Vulcan XC-72 did not significantly affect the support's microstructure. The codeposition of ionomer in the CL strongly influenced its porosity, covering pores < 20 nm, which are ascribed to the pores within the primary carbon particles (pore sizes < 2 nm) and to the pores within agglomerates of the particles (pore sizes of 2-20 nm).
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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 | 0.000 | 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".