Development of Non-Spherical Platinum Nanoparticles on Carbon Supports for Oxygen Reduction Reaction
Bibliographic record
Abstract
Proton exchange membrane fuel cells are anticipated to play an important role in decarbonizing the global energy system, but the performance of platinum (Pt) catalysts must be improved to make this technology more economical. Studies have identified non-spherical Pt nanoparticles on carbon supports as promising approaches to address this challenge. However, to realize the full benefits of these strategies, the catalyst synthesis procedures must be successfully simplified and scaled up, and the catalyst must perform well in half and full-cell tests. In this study, a surfactant-free one-pot method is developed to synthesize non-spherical Pt nanoparticles on Ketjen Black carbon, which is either non-treated (Pt/KB), acid-treated (Pt/KB-O), or nitrogen-doped (Pt/KB-N). The catalysts are synthesized in both small and large batches to determine the effect of scaling up the synthesis procedure. The nitrogen-doped carbon support shows a nearly identical morphological structure with uniform distribution of non-spherical Pt nanoparticles for both small and large batches’ synthesis compared with non-treated and acid-treated carbon samples. The comparative oxygen reduction reaction (ORR) activity shows that the Pt/KB-N prepared in small and large batches has better ORR activity, which is likely caused by uniformly distributed non-spherical Pt nanoparticles on the nitrogen-doped carbon support. All three catalysts show similar ORR durability, testing from 0.5–1.0 V, while Pt/KB-O displays slightly better durability from 1.0–1.5 V for carbon corrosion. These results will help inform the implementation of shape-controlled Pt catalysts on modified carbon supports in large scale.
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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 | 0.001 |
| 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".