The Influence of Pt Loading, Support and Nafion Content on the Performance of Direct Methanol Fuel Cells: Examined on the Example of the Cathode
Bibliographic record
Abstract
Abstract Nano‐sized Pt colloids were prepared using the polyol method and supported on Ketjen black EC 600J (KB), Vulcan XC‐72 (VC) and high surface area graphite 300 (HG). The effects of the Nafion ionomer content, and the Pt loading of the cathode catalyst layer as well as the Pt loading on the support on the performance of direct methanol fuel cells (DMFCs), were studied. The membrane electrode assemblies (MEAs) were analysed using current–voltage curves, cyclic voltammetry, electrochemical impedance spectroscopy (EIS) and adsorbed CO stripping voltammetry. Optimum Nafion to carbon (N/C) ratios (N/C being defined as the weight ratio of the Nafion ionomer to the carbon) were determined. The optimum N/C ratios were found to depend on the support as follows, 1.4, 0.7 and 0.5 for Pt/KB, Pt/VC and Pt/HG, respectively and to be independent of the Pt/C loading range of 20–80 wt% tested in this work. The highest DMFC performances, as well as the highest electrochemical active surface areas, and improved gas diffusivities, were achieved using these ratios. For the catalysts prepared in this work, the average Pt crystallite size was found to decrease with increasing surface area of the support for a particular Pt loading. MEAs made using KB as support and the optimal N/C ratio of 1.4 showed the best performances, i.e. higher than the VC and HG supports for any N/C ratio. The highest DMFC performance was observed using 60 wt% Pt on KB cathode electrodes of 1 mg Pt cm–2loading and an N/C value of 1.4. For all three supports studied, the 60 wt% Pt on carbon loading resulted in the best DMFC performance. This may be linked to the Pt particle size and catalyst preparation method used in this work. In comparison to literature results, high DMFC performances were achieved using relatively ‘low' Pt and Ru loadings. For example, a maximum power density of >100 mW cm–2at 60 °C was observed using a 1 mg Pt cm–2cathode loading and a 2 mg PtRu cm–2anode loading.
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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) | 0.001 | 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 | 0.000 |
| 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".