Hydrocarbon proton conducting polymers for fuel cell catalyst layers
Bibliographic record
Abstract
Proton exchange membrane fuel cells (PEMFCs) employing proton conducting membranes are promising power sources for automotive applications. Perfluorosulfonic acid (PFSA) ionomer represents the state-of-the-art polymer used in both the membrane and catalyst layer to facilitate the transport of protons. However, PFSA ionomer is recognized as having significant drawbacks for large-scale commercialization, which include the high cost of synthesis and use of fluorine-based chemistry. According to published research much effort has been directed to the synthesis and study of non-PFSA electrolyte membranes, commonly referred to as hydrocarbon membranes, which has led to optimism that the less expensive proton conducting membranes will be available in the not-so-distant future. Equally important, however, is the replacement of PFSA ionomer in the catalyst layer, but in contrast to membranes, studies of catalyst layers that incorporate a hydrocarbon polyelectrolyte are relatively sparse and have not been reviewed in the open literature; despite the knowledge that hydrocarbon polyelectrolytes in the catalyst layer generally lead to a decrease in electrochemical fuel cell kinetics and mass transport. This review highlights the role of the solid polymer electrolyte in catalyst layers on pertinent parameters associated with fuel cell performance, and focuses on the effect of replacing perfluorosulfonic acid ionomer with hydrocarbon polyelectrolytes. Collectively, this review aims to provide a better understanding of factors that have hindered the transition from PFSA to non-PFSA based 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".