Fe–N–C Boosts the Stability of Supported Platinum Nanoparticles for Fuel Cells
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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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 | 0.000 | 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) | 0.001 | 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".