(Plenary) Doing More with Less: Challenges for Catalyst Layer Design
Bibliographic record
Abstract
Proton exchange membrane (PEM) fuel cells are being developed as alternative energy sources for both residential and automotive application. In order for this technology to become fully commercial, the reduction of cost and improvements in performance and durability of PEM fuel cells membrane electrode assemblies (MEAs) are still required. To address the requirement for further cost reduction the Pt loading of the cathode catalyst layer (CCL) needs to be reduced to 0.2- 0.1 mg/cm2 while maintaining high efficiency of Pt-utilization at high power densities. Consequently, it becomes increasingly important to not only develop new catalyst materials, but also optimize the 3D structural arrangement of the CCL components such as catalyst, ionomer and void space so that all critical functionalities can be achieved simultaneously and optimize PGM utilization. In general terms this entails to provide sites catalytically active for ORR, and to further provide transport to/these sites for the reactants O2, protons, electrons and products H2O and heat, respectively. In order to be able to design CCL structures that meet the performance and durability requirements, it is necessary to obtain a better understanding of structure versus performance relationships. This requires the capability to fabricate different CCL structures, to characterize the spatial distribution of all components within the catalyst layer1,2 (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 a model based understanding of the relationship between CCL structure and CCL performance and durability3. References A. P. Hitchcock, V. Berejnov, V. Lee, D. Susac and J. Stumper, “In situ methods for analysis of polymer electrolyte membrane fuel cell materials by soft X-ray scanning transmission x-ray microscopy”, Microscopy & Microanalysis 20(S3) 1532 (2014) V. Lee, V. Berejnov, M. West, S. Kundu, D. Susac, J. Stumper, R.T. Atanasoski, M. Debe and A.P. Hitchcock, “Scanning Transmission X-ray Microscopy of Nano Structured Thin Film Catalysts for Proton-Exchange-Membrane Fuel Cells” J. Power Sources 263 163 (2014) S. G. Rinaldo, W. Lee, J Stumper, M. Eikerling: “Nonmonotonic dynamics in Lifshitz-Slyozov-Wagner theory: Ostwald ripening in nanoparticle catalysts” Physical Review E 86, 041601 (2012)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.031 |
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".