Decoupling Membrane Electrode Assembly Materials Complexity from Fuel Cell Performance through Image‐Based Multiphase and Multiphysics Modelling
Bibliographic record
Abstract
Abstract Proton exchange membrane fuel cells (PEMFCs) are important clean energy technology, yet the material and structural complexity of their membrane electrode assemblies (MEAs) can hamper the development of next‐generation structures, as even a subtle change to one component can have a significant impact on others. Mathematical modelling of PEMFC MEAs proves to be one of the few techniques able to decouple this complexity, but the available models are commonly based on over‐simplified structures meaning they are less able to inform material design. In this study, an advanced image‐based modelling approach is developed to reveal the interplay of material changes in PEMFC MEAs. Using high‐temperature PEMFCs as an example system, advanced structural imaging techniques are used to produce a detailed 3D MEA reconstruction which forms the basis for the multiphase and multi‐physics model. This allows both the prediction of cell performance and the decoupling the impact of changes to individual structures or components (such as membrane pores, catalyst cracks, and phase migration), on cell behaviour. These phenomena can then be selectively ‘re‐coupled’ to deconvolute the interplay of different materials employed within operational cells. The resulting insights provide a mechanistic understanding of MEA performance, guiding the design and optimisation of future PEMFCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".