Microscopy Supported Multi-Scale Modeling of PEM Fuel Cells
Bibliographic record
Abstract
Proton exchange membrane fuel cells (PEMFCs) are being developed as alternative energy sources for both residential and automotive applications. For this technology to reach its full commercial potential, however, a significant reduction in cost is still required while the performance and durability of PEMFCs being maintained or improved. The key in this process is continuous improvement of membrane electrode assembly (MEA) through both the development of new materials and the optimization of the 3D structural arrangement of individual MEA components [1,2] . Structural optimization of MEA remains to be challenging due to multiple materials interacting across 6 orders of magnitude in their characteristic length scales, from several nanometers for the catalyst particle size to hundreds of micrometers for carbon fibers in porous transport layer. In recent years, several microscopy techniques have become widely available to characterize the three-dimensional (3D) structure of PEMFC catalyst layers. Due to the heterogeneous nature of the PEMFCs sample, a correlative, multi-scale imaging protocol is developed in this work. The following technology is used where their corresponding resolution limit is pushed. Transmission Electron Microscope tomography, TEMt [3] (0.60nm X 0.60nm X 0.60nm Voxel Size) Focussed Ion Beam - Scanning Electron Microscope tomography, FIB-SEM [4] (2.5nm X 2.5nm X20 nm Voxel Size) Micro and Nano X-ray Computed Tomography, NanoCT [5] (367nm X 367nm X 367nm Voxel Size) Each technique by itself supports valuable insight to be obtained on the investigated structure at the corresponding length scale. By combining 3D imaging data at multiple scales, an unified structural characterization workflow is developed. Furthermore, an up scaling approach is developed based on TEMt, SEM and NanoCT reconstruction of catalyst layer, micro-porous layer and porous gas transport layer of a PMEFC cathode. Structural properties and effective transport properties are computed at each scale via image-based numerical simulation. Effective transport properties are up-scaled. MEA performances are predicted based on the obtained effective quantities at micro-scale. References [1] S. Thiele, T. Fürstenhaupt, D. Banham, T. Hutzenlaub, V. Birss, C. Ziegler, R. Zengerle, Multiscale tomography of nanoporous carbon-supported noble metal catalyst layers, J. Power Sources. 228 (2013) 185–192. [2] S.W. Peterson, The Effect of Microstructure On Transport Properties of Porous Electrodes, Brigham Young University, 2015. [3] H. Jinnai, R.J. Spontak, Transmission electron microtomography in polymer research, Polymer (Guildf). 50 (2009) 1067–1087. [4] C. Ziegler, S. Thiele, R. Zengerle, Direct three-dimensional reconstruction of a nanoporous catalyst layer for a polymer electrolyte fuel cell, J. Power Sources. 196 (2011) 2094–2097. [5] W.K. Epting, J. Gelb, S. Litster, Resolving the Three-Dimensional Microstructure of Polymer Electrolyte Fuel Cell Electrodes using Nanometer-Scale X-ray Computed Tomography, Adv. Funct. Mater. 22 (2012) 555–560. Figure 1
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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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".