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 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".