Gas diffusion layer characterization and microstructural modeling in polymer electrolyte fuel cells
Bibliographic record
Abstract
Polymer electrolyte fuel cells (PEFCs), as promising clean energy power sources, are potential substitutes not only for stationary power generation but also for mobile applications specifically in transportation due to their high power density and performance as well as lack of pollutants. PEFC vehicles are at the dawn of commercialization, but still, cost, performance, and durability of current PEFCs need to be further improved to facilitate vast market integration especially under high current density conditions. Pursuant to this goal, comprehensive multidisciplinary understanding of multiphase transport of mass, heat, and electricity in the PEFC constituents including the gas diffusion layer (GDL), as the centerpiece of this thesis, will help to make progress towards material optimization and subsequently fuel cell performance improvements. The GDL transport capability is determined by its effective transport properties which are strongly dependent on its morphological, microstructural, and physical characteristics. Therefore, accurate knowledge regarding the correlation between the GDL microstructure and its transport properties is essential for improving the performance and durability of PEFCs as well as for material optimization, fuel cell design, and prototyping in the area of fuel cell development and manufacturing. In this context, this thesis aims to develop a fast and cost-effective design tool for GDL microstructural modeling and transport properties simulation. Given the limitations of experimental, analytical, and tomographic techniques, stochastic microstructural model development to retrieve the heterogeneous GDL microstructure is a more reliable and flexible tool for GDL material design and prototyping assignments to reduce cost and time of the design cycle. Inspired by the randomness of the GDL porous media structure and its fabrication process, the GDL microstructure is virtually reconstructed as a collection of stochastic processes to provide a robust representation of the structure. The technique of stochastic microstructural reconstruction relies on statistical correlation functions which describe the probabilities of the porous media constituents’ distribution and aim to encompass all the details of the porous media. The obtained 3D digitized realizations of the stochastic model are then used as a domain for numerical computation of transport properties. In this thesis, a unique stochastic GDL microstructural modeling framework inspired by manufacturing information and characterization data is developed in which all GDL substrate and MPL components are resolved, and thoroughly validated with literature and measured data for a variety of MPL-coated GDLs. The effects of PTFE loading and liquid water saturation on the GDL substrate anisotropic transport properties for both gas and liquid phases are found to be highly coupled and are therefore simulated and analyzed jointly. Furthermore, a parametric study is conducted to investigate the effect of MPL pore morphology composition on the MPL and MPL-coated GDL transport properties. The validated stochastic design tool can be used as a fast and accurate framework for reconstructing GDL porous materials and understanding the correlation between the GDL morphology and transport properties. This paves the way for development of improved GDL materials with desired transport properties in modern PEFCs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".