Factors Influencing Electrochemical Properties and Performance of Hydrocarbon-Based Electrolyte PEMFC Catalyst Layers
Bibliographic record
Abstract
Cathode catalyst layers (CLs) for proton exchange membrane fuel cells (PEMFCs) incorporating sulfonated poly(ether ether ketone) (SPEEK) solid polymer electrolyte were prepared and studied in a fuel cell operated at and 100% relative humidity. SPEEK-based CLs were found to exhibit higher protonic resistance, lower effective usage of Pt, and lower fuel cell performance compared to Nafion-based cathodes. A method of fabrication was developed to achieve a homogeneous distribution of SPEEK and polytetrafluoroethylene (PTFE) throughout the catalyst layer. Homogeneously prepared SPEEK-based CLs exhibited a higher electrochemically active surface area and lower protonic resistance but lower porosity and inferior water management compared to those prepared using a traditional two-step fabrication method, wherein SPEEK ionomer was impregnated into a preformed catalyst layer incorporating sintered PTFE and . The choice of SPEEK electrolyte over Nafion was shown to adversely affect the bulk proton conductivity of the electrolyte inside the catalyst layer. This can be offset by increasing the SPEEK content in the catalyst layer but with the penalty of increased flooding and a larger resistance to gas transport.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".