Self‐Templated Hierarchically Porous Carbon Nanorods Embedded with Atomic Fe‐N<sub>4</sub> Active Sites as Efficient Oxygen Reduction Electrocatalysts in Zn‐Air Batteries
Bibliographic record
Abstract
Abstract Iron‐nitrogen‐carbon materials are being intensively studied as the most promising substitutes for Pt‐based electrocatalysts for the oxygen reduction reaction (ORR). A rational design of the morphology and porous structure can promote the accessibility of the active site and the reactants/products transportation, accelerating the reaction kinetics. Herein, 1D porous iron/nitrogen‐doped carbon nanorods (Fe/N‐CNRs) with a hierarchically micro/mesoporous structure are prepared by pyrolyzing the in situ polymerized pyrrole on the surface of Fe‐MIL‐88B‐derived 1D Fe2O3 nanorods (MIL: Material Institut Lavoisier). The Fe2O3 nanorods not only partially dissolve to generate Fe3+ for initiating polymerization but serve as templates to form the 1D structure during polymerization. Furthermore, the pyrrole coated Fe2O3 nanorod architecture prevents the porous structure from collapsing and protects Fe from aggregation to yield atomic Fe‐N4 moieties during carbonization. The obtained Fe/N‐CNRs display exceptional ORR activities (E1/2 = 0.90 V) and satisfactory long‐term durabilities, exceeding those for Pt/C. Furthermore, the unprecedented Fe/N‐CNRs catalytic performance is demonstrated with Zn‐air batteries, including a superior maximum power density (181.8 mW cm−2), specific capacity (998.67 W h kg−1), and long‐term durability over 100 h. The prominent performance stems from the unique 1D structure, hierarchical pore system, high surface area, and homogeneously dispersed single‐atom Fe‐N4 moieties.
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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".