High Performance, High Catalyst-Efficiency Hydrocarbon Fuel Cells
Bibliographic record
Abstract
While perfluorinated sulfonic acid (PFSA) membrane and ionomer materials such as Nafion® form the standard for high-performance proton-exchange membrane fuel cells (PEMFCs), the limited and difficult chemistry of perfluorinated materials hampers further material development to extended fuel cell performances and lifetimes, while the high cost of perfluorinated materials contributes to the cost barrier limiting the ubiquity adoption of fuel cells for energy generation. One method of reducing cost is the US Department of Energy target of 0.0625-0.125 mg PGM/cm2 as a catalyst loading. However, at low to ultra-low catalyst loadings, PFSAs exhibit a substantial increase in resistivity attributed to oxygen mass transport losses, particularly in the far Ohmic region of polarization data, causing disproportionate reductions to achievable power densities. Hydrocarbon proton-exchange materials offer the potential for solutions to material issues inherent to PFSAs, with established, varied chemistry allowing for versatile material innovation. Properties such as lower fuel crossover and improved operation in desirable operational conditions such as reduced relative humidity (RH) or high temperature has been well established. Together with the lower cost of basic materials and the ability to recycle catalyst layers, these properties make the demonstration of high performance, fully hydrocarbon PEMFC operation significantly of interest to the field. Here, hydrocarbon proton-exchange materials are shown that exhibit significant radical stability and high conductivities. Furthermore, these materials form thin membranes and are soluble in low-boiling, polar solvents necessary for incorporation as ionomer into catalyst inks that create high-quality catalyst layers. MEAs and catalyst layers were formed, incorporated into fuel cells, and characterized in situ by IV polarization, CV, CA, LSV, and EIS, and ex situ by mercury porosimetry and SEM. As ionomers, these exhibit reduced mass transport losses compared to PFSA ionomers, a result of substantially smaller increases to oxygen mass transport losses than PFSAs exhibit as catalyst layer loadings are decreased. As fully hydrocarbon fuel cells, improved interfaces lead to improved water transport and lower total resistivity attributable to membrane and ionomer congruency, thereby achieving higher power densities than directly comparable PFSA references using rigorously optimized conditions and catalyst loadings. These high-performance hydrocarbon proton-exchange materials may thereby represent a fully hydrocarbon alternative to PFSA materials for hydrogen fuel cells.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".