Novel Graphene Foam Microporous Layers for PEM Fuel Cells: Interfacial Characteristics and Comparative Performance
Bibliographic record
Abstract
Abstract The microporous layer (MPL) provides many beneficial properties for performance improvement of H2 PEM fuel cells, particularly with respect to water management and two‐phase (gas/liquid) flow dynamics. However, the interface between the catalyst layer (CL) and MPL could be a source of additional overpotential losses due to poor electronic conductivity and/or mass transfer limitations. This is particularly important for low loading CLs which may suffer from spatial disconnect with the micro‐scaled features of conventional MPLs. In an effort to better understand the factors influencing the MPL|CL interface, the conventional carbon MPL is comparatively studied with respect to three alternative layers: graphene foam, perforated graphitic sheet and perforated stainless steel. The graphene foam shows beneficial interfacial properties that contribute to electrode kinetic and ohmic improvements during polarization. This can be attributed to the graphene's ability to conform at local length scales due to its unique flake‐like structure (achieved upon compression), an ability to intimately adhere to the CL and the layer's superior conductivity. Further investigation through single cell performance tests supports the application of the graphene foam as an MPL alternative. The results also highlight the interplay of various factors that influence the MPL|CL interface and ultimately the overall polarization performance, such as: morphology, conductivity, connectivity, compression and adhesive effects between layer components.
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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 | 0.001 | 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".