Pore Network Modeling and Synchrotron Imaging of Liquid Water in the Gas Diffusion Layer of Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
Polymer electrolyte membrane (PEM) fuel cells operate at levels of high humidity, leading to condensation throughout the cell components. The porous gas diffusion layer (GDL) must not become over-saturated with liquid water, due to its responsibility in providing diffusion pathways to and from the embedded catalyst sites. Due to the opaque and microscale nature of the GDL, a current challenge of the fuel cell industry is to identify the characteristics that make the GDL more or less robust against flooding. Modeling the system as a pore network is an attractive investigative strategy; however, for flooding simulations to provide meaningful material comparisons, accurate GDL topology and condensation distributions must be provided. The focus of this research is to provide the foundational tools with which to capture both of these requirements. The method of pore network modeling on topologically representative pore networks is demonstrated to describe flooding phenomena within GDL materials. A stochastic modeling algorithm is then developed to create pore spaces with the relevant features of GDL materials. Then, synchrotron based X-ray visualization experiments are developed and conducted to provide insight into condensation conditions.It was found that through-plane porosity distributions have significant effects on the GDL saturation levels. Some GDL manufacturing processes result in high porosity regions which are predicted to become heavily saturated with water if they are positioned between the condensation sites and the exhaust channels. Additionally, it was found that fiber diameter and the volume fraction of binding material applied to the GDL have significant impacts on the GDL heterogeneity and pore size distribution. Representative stochastic models must accurately describe these three material characteristics. In situ, dynamic liquid water behavior was visualized at the Canadian Light source, Inc. synchrotron using imaging and image processing techniques developed for this work. Liquid water primarily originated beneath the flow field landings, sometimes spreading laterally into the less compressed regions of the GDL beneath the flow field channels. Independent water clusters were tightly packed within the GDL, rarely occupying more than 1 mm2 of planar area. These tools and observations provide the capability to predictively design high performance GDL materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".