Novel Approach to Measure Key Structural Parameters of PEM Fuel Cell Catalyst and Gas Diffusion Layer Based on Archimedes Principle
Bibliographic record
Abstract
Commercialization of fuel cells and demand for large scale manufacturing of their components necessitates the design of novel characterization methods which produce consistent results in a timely and cost efficient manner. In this work, we report a simple approach to measure the porosity, pore volume and thickness of catalyst and gas diffusion layers based on the principle of buoyancy in different fluids. Moreover, the method allows for obtaining the ionomer content of the catalyst layer. By applying this method on a porous PTFE substrate with known pore surface area and varying ionomer content, we are able to measure the density of the ionomer film vs. thickness at high (nm) resolution. Water sorption measurements on these model samples yield the maximum water content as a function of ionomer thickness. Water sorption measurements made on individual components of the catalyst layer (Pt/C and bulk ionomer) were also compared against those made on a catalyst layer. We observed a discrepancy between water uptake of individual constituents and catalyst layer. We utilized such difference in measured and expected water uptake to estimate the thickness of ionomer in the catalyst layer. This is the first attempt to estimate the thickness of ionomer in catalyst layer using a bulk scale ex-situ method. Overall, we show that this method is a lower cost and faster technique for measuring key characteristics of porous layers when compared with alternative methods such as electron microscopy and spectroscopy techniques. Furthermore, it can be easily adopted by industry and large manufacturing divisions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".