New Synthesis route of Iron-Based Catalyst for Electrochemical Oxygen Reduction Reaction
Bibliographic record
Abstract
Among existed oxygen reduction electrocatalysts, iron-based catalysts have shown great advantages of low cost and extraordinary reactivity, which are even comparable to commercialized platinum based catalysts. However, the propensity of iron catalysts to aggregate and passivate has emerged as a fundamental barrier to high-power fuel cell applications. In this study, biomass egg yolk derived carbon nanotubes were designed as an armor to host iron complexes, offering multiple active sites such as Fe-N x , Fe 3 C, Fe 2 P for efficient oxygen reduction reaction (ORR). Although the true active sites of iron-based catalysts on the enhanced ORR activities is still under debates, a consensus on the contributions of Fe-N x active center has been reached via a smart material design in this work, which enables ORR onset potential at 0.9 V vs. RHE with excellent four-electron selectivity in alkaline media. Meanwhile, the state-of-the-art carbon shells promote the performance stability remarkably (retaining above 96% of its activity after 27 hours).
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".