Fe <sub>3</sub> O <sub>4</sub> Templated Pyrolyzed Fe−N−C Catalysts. Understanding the role of N‐Functions and Fe <sub>3</sub> C on the ORR Activity and Mechanism
Bibliographic record
Abstract
Abstract Pyrolyzed non‐precious metal catalysts have been proposed as an alternative to substitute the expensive and scarce noble metal catalysts in several conversion energy reactions. For the oxygen reduction reaction (ORR), the pyrolyzed catalyst M−N−C (M: Fe or Co) presents remarkable catalytic activity in acid and alkaline media. These pyrolyzed materials show a high heterogeneity of active sites being the most active in the MNx moieties. The activity and stability of these catalysts are also conditioned by other structural parameters such as the area, the N‐doping, and by the presence of metal particles. In this study, we explore the use of Fe 3 O 4 nanoparticles as templates and as iron sources to synthesize Fe−N−C. The best performance for the ORR in acidic media was reached with the catalysts using nanoparticles covered by PANI and iron salts as the precursor, with an onset potential of 0.85 vs. RHE and a direct 4‐electrons mechanism. We corroborated the use of the catalysts’ redox potential as reactivity descriptors and discussed the detrimental role of the presence of Fe 3 C metallic particles in the mechanism. Based on the experimental results, we performed DFT calculations to explore the influence of N‐doped species on the electronic density of the iron centers of FeN4 active sites, and we propose a theoretical model for increasing the activity based on the distance and ratio of N‐doping to iron center.
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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".