Fluorescence Microscopy for the Measurement of the Surface Properties of the Gas Diffusion Layers of Fuel
Bibliographic record
Abstract
One of the major problems of current proton exchange membrane (PEM) fuel cells is water management. The gas diffusion layer (GDL) of the fuel cell plays an important role in water management since humidification and water removal are both achieved through the GDL. Various numerical models developed to illustrate the multiphase flow and transport in the fuel cell require the accurate measurement of the GDL properties (wettability and surface energy). In a recent study, the capillary penetration technique has been used to measure indirectly the wettability of the GDL based on the experimental height penetration of the sample liquid into the porous sample. In essence, a high resolution microscope/camera was used to detect the rate of penetrated height into the sample GDL. The shortcoming of this type of visualization is that it can only be used for thin hydrophilic GDL samples with zero Teflon loadings. For the thick and high Teflon loading GDLs, there is not enough contrast to detect the height of the penetrated liquid. In the real fuel cells, the GDLs are made of the micro-porous and macro-porous layers with an optimum Teflon loading. Therefore, it is required to develop a new experimental methodology capable of detecting the rate of penetration and as a result the wettability of GDLs samples used in fuel cells. In this paper, the fluorescence microscopy technique is integrated into the experimental setup of the capillary penetration method to improve the contrast between the wetted and non-wetted area. The fluorescence setup uses a powder die, dissolved in the test fluid, which is excited by a concentrated ultraviolet light illuminated in the vertical manner. To acquire the profile images of the penetrated liquid, an optical mirror was used. This new setup has the added advantage of providing a visual representation of the different regimes of penetration (e.g., the fingering effect reported for the pathways of the liquid penetrated into the GDLs) that are defined by the capillary number and mobility ratio of each fluid. Since the GDL samples used in this study are relatively hydrophobic (e.g., with 40% Teflon loadings), the pattern of liquid penetration is not uniform. Thus, an image analysis program was developed to determine the average height of penetration based on the area under the entire wetted area. The general Washburn equation was then used to fit the extracted height data and provide the average internal contact angle for each test liquid.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".