Unravelling the Stability Stressors of Atomically Dispersed Fe–N–C Oxygen Reduction Catalysts
Bibliographic record
Abstract
Enhancing the catalytic stability of Fe–N–C catalysts for cathodic oxygen reduction in proton-exchange membrane fuel cells (PEMFCs) necessitates an in-depth understanding of their degradation mechanisms. This study identifies key stressors affecting the stability of Fe–N–C catalysts, specifically acidic environment, oxygen (O 2 ), and reactive oxygen species (ROS). Through ex situ/operando experiments, we show that the oxidation of local carbon by acidic environment + O 2 + ROS, along with the demetalation of catalytic FeN x C y sites by O 2 or O 2 + ROS, is the primary factor responsible for the initial fast degradation of Fe–N–C catalysts. The demetalation of FeN x C y sites, influenced by O 2, in particular by O 2 + ROS, leads to the subsequent gradual degradation of Fe–N–C. Notably, FeN 4 C 12 -type active sites are more susceptible to demetalation than FeN 4 C 10 -type sites in O 2 or O 2 + ROS. Our findings indicate that, besides constructing more stable FeN x C y sites, preventing local carbon oxidation and scavenging of ROS are all critical for maintaining the stability of Fe–N–C catalysts.
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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) | 0.000 | 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 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".