Composite-supported Pt catalyst and electrosprayed cathode catalyst layer for polymer electrolyte membrane fuel cell
Bibliographic record
Abstract
A major limitation of the conventional polymer electrolyte membrane fuel cell (PEMFC) catalysts is the fast oxidative degradation of their carbon black supports. Complete replacement of carbon black is difficult because of its low-cost and high electrical conductivity. Reported here are the development and optimization of composite-supported Pt catalysts and the electrosprayed cathode catalyst layer with these catalysts for PEMFC. These catalysts are supported by a composite of carbon black (Vulcan XC-72R) and the electrochemically much more stable carbon-embedded niobium-doped titanium dioxide nanofibers (C/Nb0.1Ti0.9O2). Four different catalyst supports with 20 wt.% Pt were prepared by air spraying and electrospraying to compare their activity and stability. Vulcan XC-72R and C/Nb0.1Ti0.9O2 were tested as pristine support materials for comparison as well as 1:3 and 3:1 mixtures by weight of the two pristine support materials (composite supports). The amount of Nafion in the catalyst ink was optimized for each catalyst layer by a volumetric method. An increase in carbon black content of the support layer from 0% to 100% increases the performance of these catalysts in H2/air PEMFCs but also increases the loss of oxygen reduction reaction mass activity. The best balance between PEMFC performance and durability was obtained for the Pt catalyst with 25% carbon black in the support layer, while the highest initial oxygen reduction reaction mass activity was obtained for the catalyst with 75% carbon black content. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".