Fe–N–C Boosts the Stability of Supported Platinum Nanoparticles for Fuel Cells
Why this work is in the frame
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Bibliographic record
Abstract
The poor durability of Pt-based nanoparticles dispersed on carbon black is the challenge for the application of long-life polymer electrolyte fuel cells. Recent work suggests that Fe- and N-codoped carbon (Fe–N–C) might be a better support than conventional high-surface-area carbon. In this work, we find that the electrochemical surface area retention of Pt/Fe–N–C is much better than that of commercial Pt/C during potential cycling in both acidic and basic media. In situ inductively coupled plasma mass spectrometry studies indicate that the Pt dissolution rate of Pt/Fe–N–C is 3 times smaller than that of Pt/C during cycling. Density functional theory calculations further illustrate that the Fe–N–C substrate can provide strong and stable support to the Pt nanoparticles and alleviate the oxide formation by adjusting the electronic structure. The strong metal–substrate interaction, together with a lower metal dissolution rate and highly stable support, may be the reason for the significantly enhanced stability of Pt/Fe–N–C. This finding highlights the importance of carbon support selection to achieve a more durable Pt-based electrocatalyst for fuel cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it