Concave Structural Carbon Co‐Doped with Iron Atom Pairs and Nitrogen as Ultra‐High Performance Catalyst Toward Oxygen Reduction
Bibliographic record
Abstract
Abstract It is crucial to rationally design and synthesize atomic‐scale transition metal‐doped carbon catalysts with high electrocatalytic activity to achieve a high‐efficient oxygen reduction reaction (ORR). Herein, an electrocatalyst comprised of Fe–Fe dual atom pairs and N‐doped concave carbon are reported (N‐CC@Fe DA) that achieves ultrahigh electrocatalytic ORR activity. The catalyst is prepared by a gaseous doping approach, with zeolitic imidazolate framework‐8 (ZIF‐8) as the carbon framework precursor and cyclopentadienyliron dicarbonyl dimer as the Fe–Fe atom pair precursor. The catalyst exhibits high cathodic ORR catalytic performance in an alkaline Zn/air battery and proton exchange membrane fuel cell (PEMFC), yielding peak power densities of 241 mW cm−2 and 724 mW cm−2, respectively, compared to 127 mW cm−2 and 1.20 W cm−2 with conventional Pt/C catalysts as cathodes. The presence of Fe atom pairs coordinate with N atoms is revealed by X‐ray photoelectron spectroscopy (XPS) and X‐ray absorption spectroscopy (XAS) analysis, and Density Functional Theory (DFT) calculation results show that the Fe–Fe pair structure is beneficial for adsorbing oxygen molecules, activating the O─O bond, and desorbing OH* intermediates formed during oxygen reduction, resulting in a more efficient oxygen reaction. The findings may provide a new pathway for preparing ultra‐high‐performance doped carbon catalysts with Fe–Fe atom pair structures.
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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".