Bibliographic record
Abstract
The role of the ionomer in catalyst layers is critical to the performance of PEM fuel cells. Attention needs to be paid not only to the inherent properties of the ionomer but also to the choice of dispersing solvent and the catalyst support, as these also control the porosity and proton conductivity of the catalyst layer in conjunction with the ionomer. The ionomer also influences electrochemical FC kinetics through its influence on surface adsorption and gas permeability. Mass transport of oxygen in cathode catalyst layers (CCLs) is of particular importance in achieving high current densities in proton exchange membrane fuel cells. The technical push toward low platinum loading in CCLs has resulted in a disproportionate transport resistance attributed to oxygen transiting through thin ionomer films to reach active platinum sites. The replacement of PFSA ionomer in the catalyst layer with hydrocarbon ionomers is thus particularly challenging as this often decreases electrochemical fuel cell kinetics and mass transport. For these reasons, permeability phenomena of oxygen at the ionomer/platinum interface has gained renewed interest. Electrochemical techniques, such as potential step chronoamperometry at microelectrodes, will be shown to be a useful to probe oxygen diffusion and oxygen solubility at catalyst/membrane interface. Data obtained under different conditions and with different ionomers is useful for understanding oxygen transport resistance through ionomer films in the context of low platinum loaded cathode catalyst layers
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".