Impacts of Catalyst Ink Composition and Wet Film Thickness on Fuel Cell Catalyst Layers Fabricated by Direct Film Coating Method
Bibliographic record
Abstract
Utilizing a direct film coating method (DFCM), such as doctor blade coating, offers a promising approach for efficient and scalable catalyst layer (CL) production for fuel cells. To further widen the understanding of lab-scale DFCM, the present research investigates how different Pt-based catalyst ink formulations coated via doctor blade coating with varying blade gap thickness (BGT) affect the CL quality and catalyst loading. In total, 120 CL samples were prepared by coating 20 different catalyst ink formulations with varying solids content, ionomer-to-carbon (I/C) ratio, and water-to-isopropanol solvent ratio with BGTs of 75, 125, and 200 μm. Inspection of these samples showed that the solvent ratio affects the coating uniformity, with the most uniform films achieved with a ratio of 1.67 or greater. Furthermore, increasing the I/C ratio for a given solids content ink formulation decreases the Pt loading, whereas an I/C ratio above or below 1.0 reduces cell performance due to mass transport or proton conductivity impacts, respectively. In addition, a relationship factor and equations are presented to estimate the solid weight and catalyst loading of the fabricated CL based on the ink formulation and BGT. Overall, this work provides important guidance for lab-scale DFCM fabrication of industrially relevant CLs.
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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".