Impact of carbon supports on the Pt-based catalyst activity and fuel cell performance under varied operational conditions
Bibliographic record
Abstract
• A one-pot method is used to prepare Pt nanoparticles on various carbon supports. • Pt particles are characterized to be uniformly deposited with the size of 2 ∼ 3 nm. • Half and single-cell tests are performed to evaluate the catalytic performance. • Incorporating graphene into Ketjen black facilitates gas convection and diffusion. • A high peak power density of 1.17 W/cm 2 is achieved under 100 % relative humidity. Proton exchange membrane fuel cells (PEMFCs) play a pivotal role in advancing sustainable energy systems, with catalysts serving as critical components for their effectiveness and efficiency. While commercial Pt/C catalysts have made significant contributions, further improvements are still needed to meet the evolving requirements. Current methods for synthesizing high-performance catalysts often focus on composition design, morphology control and surface modification, while they face limitations related to the complicated preparation process and scalability. To address these issues, a facile one-pot synthesis approach is developed to prepare Pt nanoparticles on the Ketjenblack EC-600JD (Pt/Ket600), graphene nanoplatelets (Pt/GNP) and mixture of Ket600 and GNP (Pt/Ket600/GNP). The incorporation of GNP into Ket600 optimizes the structural properties of the carbon support, providing an effective platform for better dispersion of Pt nanoparticles. The high scalability of the proposed synthesis method yields over 750 mg of catalyst per batch, ensuring its applicability for large-scale production. As expected, Pt/Ket600/GNP demonstrates an optimal morphological structure, with Pt nanoparticles uniformly distributed with an average size of 2.15 nm and an overall Pt loading of approximately 46 %. The as-prepared Pt/Ket600/GNP also exhibits reduced permeation and diffusion resistivity, along with 1.26- and 1.68-fold higher mass activity for the oxygen reduction reaction, compared to Pt/Ket600 and Pt/GNP, respectively. The membrane electrode assembly with the Pt/Ket600/GNP catalyst achieves a peak power density of 1.17 W/cm 2 when operating on fully humidified hydrogen and air at 75 °C and 35 kPaG. This impressive performance highlights its strong potential for practical PEMFC applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".