PEMFC Catalyst Layers: Recent Advances in Process-Structure-Property Correlations
Bibliographic record
Abstract
Proton exchange membrane (PEM) fuel cells are being developed as alternative energy sources for both residential and automotive applications. In order for this technology to become fully commercial, reduction of cost and improvements in performance and durability of PEM fuel cells’ membrane electrode assemblies (MEAs) are still required. To address the requirements for further cost reduction, structure and composition of the cathode catalyst layer (CCL) has to be optimized in order to achieve optimum performance and durability while minimizing the platinum group metals (PGM) loading. In order to rationally design CCL structures that meet the performance and durability requirements, a better understanding of structure versus performance relationships is needed. This requires the capability to fabricate different CCL structures, to characterize the spatial distribution of all the phases within the catalyst layer1-3 (carbon, Pt, ionomer, and void), to measure the physico-chemical properties (both ex-situ and in-situ), and, finally, to use these experimental data as inputs for the development of a model-based understanding of the relationship between the CCL structure and CCL performance and durability. Recently, significant progress has been made in reliable measurement and modeling of key CCL properties such as thermal conductivity, electronic conductivity, and gas diffusivity4-7. Dependence of these properties on the CCL structure and manufacturing processes provides support for rational CCL design. References J. Wu, A.P. Hitchcock, M. Lerotic, D. Shapiro, V. Berejnov, D. Susac, J. Stumper, “4D imaging of polymer electrolyte membrane fuel cell cathodes by scanning X-ray microscopy”, Microsc. Microanal., 23, 1784 (2017). V. Berejnov, M. Saha, D. Susac, J. Stumper, M. West, A.P. Hitchcock, Advances in structural characterization using soft x-ray scanning transmission microscopy (STXM): Mapping and measuring porosity in PEM-FC catalyst layers, ECS Transactions, 80, 241 (2017). L.G.A. Melo, A.P. Hitchcock, J. Jankovic, J. Stumper, D. Susac, V. Berejnov, Quantitative mapping of ionomer in catalyst layers by electron and x-ray spectromicroscopy, ECS Transactions, 80, 275 (2017). M. Ahadi, M. Tam, M.S. Saha, J. Stumper, M. Bahrami, Thermal conductivity of catalyst layer of polymer electrolyte membrane fuel cells: Part 1 – Experimental study, J. Power Sources, 354, 207 (2017). M. Ahadi, A. Putz, J. Stumper, M. Bahrami, Thermal conductivity of catalyst layer of polymer electrolyte membrane fuel cells: Part 2 – Analytical modeling, J. Power Sources, 354, 215 (2017). M. Ahadi, M. Tam, A. Putz, J. Stumper, C. McCague, M. Bahrami, Electrical conductivity of PEM fuel cell catalyst layers: Through-plane vs. in-plane, in: European Hydrogen Energy Conference 2018, Costa del Sol, Spain, 14-16th March, paper no. (124), 206 (2018). S. Salari, C. McCague, M. Tam, M.S. Saha, J. Stumper, M. Bahrami, Accurate ex-situ measurements of PEM fuel cells catalyst layer dry diffusivity, ECS Transactions, 69, 419 (2015).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".