Investigation of pinhole defects in gas diffusion layers for the quality control of proton exchange membrane fuel cells
Bibliographic record
Abstract
Abstract The gas diffusion layer (GDL), one of the essential components of the membrane electrode assembly (MEA), plays an important role in the performance of proton exchange membrane fuel cells. With respect to this essential component and its specifications, this work intends to examine the impact of GDL defects and their effects on cell performance for component quality control. To understand how GDL defect affects its performance and to what level the defect takes effect, ex situ characterization and in situ fuel cell testing are conducted by comparing pristine and defective GDLs. While ex situ GDL properties incorporate measurements of thickness, conductivity, and permeability under compression, in situ investigation mainly involves polarization curve and electrochemical impedance spectroscopy. Among different types of GDL defects, pinholes are targeted in this work. As such, the evaluation focuses on assessing the effects of varying numbers and sizes of pinhole defects under different relative humidities (RHs). Using the state‐of‐the‐art GDLs, an improved cell performance is observed with defective GDLs (evenly distributed 40 pinholes with a diameter of 0.58 mm) under 100% RH. Results also show that the effect of pinhole defects is sensitive to RH, as well as operating current densities.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".