Characterization of Micro-Porous Layer Structure and Properties
Bibliographic record
Abstract
In order to accelerate commercialization of polymer electrolyte membrane fuel cells (PEMFCs) as a substitution to internal combustion engines in automotive applications, considerable research efforts have been devoted on the materials used in the system, notably the membrane, catalyst layer, and gas diffusion layer (GDL) of the membrane electrode assembly (MEA). The GDL is one of the vital components of the MEA that has a variety of functions significant for the overall cell performance and durability. It is typically a dual-layer carbon-based material composed of a macro-porous substrate, which usually contains carbon fibers, binder, and PTFE, and a thin, delicate micro-porous layer (MPL), which is usually made of carbon nano-particles and PTFE. The presence of the MPL supports high performance at high current densities, which is important for automotive applications. However, reliable assessment of the MPL structure and properties is a major challenge, and literature data are scarce. In the present study, a customized 3D morphological MPL characterization method developed by our group [1] is applied to analyze the structure and properties of two different MPL materials. The proposed method includes focused ion beam (FIB) milling, scanning electron microscopy (SEM), 3D reconstruction, and material property simulations in order to accurately investigate the MPL microstructure, porosity, pore size distribution, and effective transport properties [1]. Two different dual beam FIB-SEM systems are utilized and compared for high-resolution nano-tomography of the MPL samples: an older FEI Strata DB 235 and a brand new FEI Helios NanoLab TM 650. 3D reconstruction is the most prominent step of the framework, which includes image acquisition, image processing, and segmentation of the captured SEM images of the milled structure. Figure 1 illustrates the 3D structure of the MPL models. Noticeable differences are observed in the two materials; specifically, the second material appears to have a more compact structure with lower porosity. The obtained results are validated with a previously measured MPL pore size distribution (PSD) [1]. Good agreement is observed by comparing the simulated PSD of the first MPL model with the measured data. However, the second MPL model exhibits a systematic shift to smaller pore sizes. The calculated properties reveal several major differences between the two MPL materials: the second material is found to have a 9% lower porosity, smaller pore sizes, a 40% lower effective diffusivity for both oxygen and water vapor, and a 2x higher effective thermal conductivity. All of these differences are attributed to the more compact, low-porosity structure of the second MPL material, having less open pore structure responsible for diffusion of reactants and products. While the lower diffusivity may limit its reactant mass transport effectiveness at high current density operation, the higher thermal conductivity can be beneficial for thermal management of the MEA. Overall, the present MPL characterization framework is demonstrated to accurately detect small variations in the structure of different MPL materials and their impact on the effective transport properties. Acknowledgments: The research was supported by Mercedes-Benz Canada, Fuel Cell Division and the Natural Sciences and Engineering Research Council of Canada. Figure Caption: Figure 1: 3D reconstructed models for the two MPL materials analyzed in this work. A porosity difference is evident by visual comparison of the two structures. References: [1] A. Nanjundappa, A.S. Alavijeh, M. El Hannach, D. Harvey, E. Kjeang, Electrochimica Acta 110 (2013) 349–357.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".