Advanced Characterization and Quantification of Fuel Cell Electrodes Using Electron and X-Ray Microscopy Techniques
Bibliographic record
Abstract
Performance and durability of proton exchange membrane fuel cells (PEMFC) very much depend on the composition, microstructure and spatial distribution of all components in the fuel cell electrodes, especially the metal catalyst, carbon support, ionomer and pores in the catalyst layer. This dependence is even more enhanced in the case of low Pt loading fuel cells, where high performance losses occur due to increased oxygen transport resistance [1, 2], and the design of the catalyst layer is of utmost importance. Understanding the electrode microstructure and component distribution at the beginning of life (BOL), as well as their change at the end of life (EOL), after running the cells at various operating conditions, is crucial for further development of fuel cells. Conventional imaging techniques offer limited information and can often be misleading. Novel and advanced methods for spatial structural characterization and component quantification are being developed, enabled by the rapid advancement of the microscopy techniques and computational power, offering a new level of understanding for processes in fuel cells. This talk will give an overview of some of the advanced approaches reported in the literature, as well as describe novel fuel cell material characterization and quantification methods developed in our team. The topics will cover: 3D imaging and quantification of catalyst powders and resulting catalyst layers on a nano-scale using an electron tomography (ET) approach that enables spatial distribution of all phases, ionomer included, for the first time. 3D multi-scale imaging and quantification of membrane electrode assemblies (MEAs) correlatively utilizing ET, focused ion beam-scanning electron microscopy (FIB-SEM), and 3D X-ray microscopy (3D XRM), coupled with effective property simulation. Applying a novel (all-in-one) approach to visualize and quantify a number of catalyst layer parameters in BOL and EOL samples, such as Pt loading, loss and distribution, ionomer loading and I/C ratio, as well as layer porosity using transmission electron microscopy with energy dispersive spectroscopy (TEM-EDX). These advanced characterization approaches, coupled with targeted experimental testing and mathematical modeling, are key for further understanding and improving the performance and durability of fuel cells, bringing them closer to mass commercialization. A. Kongkanand and M. F. Mathias, J. Phys. Chem. Lett., 2016, 7 (7), pp 1127–1137 P. Gazdzicki, J. Mitzel, A.M. Dreizler, M. Schulze, K. A. Friedrich, Fuel Cells, 2017, 17 (5) 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".