The Impact of Subsurface and Thin Pt Layer in Nafion Membrane on H<sub>2</sub>/O<sub>2</sub> PEM Fuel Cell Performance
Bibliographic record
Abstract
Electroless deposition is a simple and scalable method to deposit a thin layer of Pt in the membrane that has been demonstrated in a number of studies [1]–[6]. Recent advancements in the deposition method has enabled deposition of an ultra-thin Pt layer in Nafion (< 200 nm) [5], but no thorough study with respect to the impact of deposition parameters on the physical structures and fuel cell performance has been reported in the literature. For fuel cell applications, it is essential to design an optimum platinized membrane structure which maximizes Pt utilization while minimizing the Pt loading. In this study, the ultra-thin electroless deposited layers with various loadings were studied and characterized physically and electrochemically. Grain size and ECSA characterization from XRD and CV analysis indicate that inter-particle electrical connectivity was improved as the Pt loading increased, until the loading reached a value of about 52 μgPt/cm2 (Figure 1). Fuel cell polarizations and constant current operation under different humidity levels were examined with the additional electroless deposited Pt layers at loadings below 45 μgPt/cm2 than for the standard MEAs. The kinetic performance of the electroless deposited Pt layers in the membrane was also examined and will be presented at the meeting. Figure 1
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".