(Invited) Catalyst Layers for Fluorine-Free Hydrocarbon PEMFCs
Bibliographic record
Abstract
Electrochemical energy conversion devices such as fuel cells are crucial for the development of renewable, sustainable alternative energy vectors. The implementation of fuel cell technology is of particular interest in portable, transportation, and stationary energy conversion sectors. There are challenges that still impede fuel cell commercialization at scale: cost, performance, and durability of the membrane-electrode-assemblies (MEA)s that comprise fuel cell stacks. MEAs typically consist of catalyst layers (CL) coated on either side of a proton-exchange membrane (PEM), and gas diffusion layers (GDL) which control water management and reactant gas mass transport. The CL provides a path for proton, electron, and gas transport to (and from) the Pt catalyst surface. Commonly, a perfluorosulfonic acid (PFSA) ionomer, e.g., Nafion® is employed as both the proton exchange membrane (PEM) and ionomer. However, perfluorinated materials are not without challenges as their manufacture utilizes controlled substances and potentially environmentally hazardous chemical feedstocks, which add to their relatively high cost. In contrast, fluorine-free hydrocarbon-based analogues may be prepared from ubiquitous, readily available chemical feedstocks, and afford much-reduced gas permeability. In this presentation, highly performing (power densities of >1 W cm-2), sulfo-phenylated poly(phenylene)PPB-H+ solid polymer electrolyte will be reported as a complete substitute for incumbent PFSA materials in PEMFCs, serving both as the electrode catalyst binder, and membrane.
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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 | 0.000 |
| 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) | 0.058 | 0.036 |
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".