Overcoming the Conductance versus Crossover Trade-off in State-of-the-Art Proton Exchange Fuel-Cell Membranes by Incorporating Atomically Thin Chemical Vapor Deposition Graphene
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Permeance–selectivity trade-offs are inherent to polymeric membranes. In fuel cells, thinner proton exchange membranes (PEMs) could enable higher proton conductance and increased power density with lower area-specific resistance (ASR), smaller ohmic losses, and lower ionomer cost. However, reducing thickness is accompanied by an increase in undesired species crossover harming performance and long-term efficiency. Here, we show that incorporating atomically thin monolayer graphene synthesized via scalable chemical vapor deposition (CVD) and tunable defect density into PEMs (Nafion, ∼5–25 μm thick) can allow for reduced H 2 crossover (∼34–78% of Nafion of a similar thickness) while maintaining adequate areal proton conductance for applications (>4 S cm –2 ). In contrast to most prior work using >50 μm symmetric Nafion sandwich structures, we elucidate the interplay of graphene defect density and Nafion proton transport resistance on the performance of Nafion|graphene composite membranes and find high-quality low-defect density CVD graphene (G) supported on Nafion 211 (∼25 μm); i.e., N211|G has a high areal proton conductance (∼6.1 S cm –2 ) and the lowest H 2 crossover (∼0.7 mA cm –2 ). Fully functional centimeter-scale N211|G fuel-cell membranes demonstrate performance comparable to that of state-of-the-art Nafion N211 at room temperature as well as standard operating conditions (∼80 °C, ∼150–250 kPa-abs) with H 2 /air (power density ∼0.57–0.63 W cm –2 ) and H 2 /O 2 feed (power density ∼1.4–1.62 W cm –2 ) and markedly reduced H 2 crossover (∼53–57%).
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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 | 0.000 | 0.001 |
| 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".