Effect of Ionomer Loading in Inket Printed Catalyst Coated Membranes on PEFC Performance
Bibliographic record
Abstract
The effect of ionomer loading on ultra-thin polymer electrolyte membrane fuel cell (PEMFC) electrodes fabricated using an inkjet printer is studied under a variety of temperatures and relative humidity conditions. Catalyst layer inks with varying amounts of Nafion were prepared and loaded in inkjet cartridges. The inks were then jetted on a Nafion membrane using an inkjet printer in order to produce fuel cell electrodes with low loadings. The Pt loading for the fabricated electrodes was estimated to be in the range of 0.009–0.01 mg/cm2 per printer pass. SEM imaging shows that the thickness of a catalyst layer fabricated using 10 passes is approximately 3 microns and the catalyst layer agglomerates are about 200nm in radius. In order to determine the optimal Nafion content for this fabrication method, the electrochemical active area and steady state performance for the fabricated catalyst coated membranes (CCMs) was measured and also compared to a conventional CCM fabricated using spray deposition. Results show that optimal performance is achieved with a 40wt% Nafion loading under the operating conditions tested. The improved performance can be attributed to decrease in cell resistance and increase in proton conductivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".