3D Porous Fe/N/C Spherical Nanostructures As High-Performance Electrocatalysts for Oxygen Reduction in Both Alkaline and Acidic Media
Bibliographic record
Abstract
Exploring inexpensive and high-performance nonprecious metal catalysts (NPMCs) to replace the rare and expensive Pt-based catalyst for the oxygen reduction reaction (ORR) is crucial for future low-temperature fuel cell devices. Herein, we developed a new type of highly efficient 3D porous Fe/N/C electrocatalyst through a simple pyrolysis approach. Our systematic study revealed that the pyrolysis temperature, the surface area, and the Fe content in the catalysts largely affect the ORR performance of the Fe/N/C catalysts, and the optimized parameters have been identified. The optimized Fe/N/C catalyst, with an interconnected hollow and open structure, exhibits one of the highest ORR activity, stability and selectivity in both alkaline and acidic conditions. In 0.1 M KOH, compared to the commercial Pt/C catalyst, the 3D porous Fe/N/C catalyst exhibits ∼6 times better activity (e.g., 1.91 mA cm –2 for Fe/N/C vs 0.32 mA cm –2 for Pt/C, at 0.9 V) and excellent stability (e.g., no any decay for Fe/N/C vs 35 mV negative half-wave potential shift for Pt/C, after 10000 cycles test). In 0.5 M H 2 SO 4, this catalyst also exhibits comparable activity and better stability comparing to Pt/C catalyst. More importantly, in both alkaline and acidic media (RRDE environment), the as-synthesized Fe/N/C catalyst shows much better stability and methanol tolerance than those of the state-of-the-art commercial Pt/C catalyst. All these make the 3D porous Fe/N/C nanostructure an excellent candidate for non-precious-metal ORR catalyst in metal–air batteries and fuel cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 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".