Nitrogen‐Doped Porous Carbon Cages for Electrocatalytic Reduction of Oxygen: Enhanced Performance with Iron and Cobalt Dual Metal Centers
Bibliographic record
Abstract
Abstract Heteroatom‐doped carbon materials are promising electrocatalysts towards the oxygen reduction reaction (ORR). In this study, dual metals (Fe an Co) and nitrogen‐codoped porous carbon cages (CHS−FeCo) were synthesized by controlled pyrolysis of silica nanoparticle‐supported melamine‐formaldehyde resin embedded with iron and cobalt precursors, followed by acid etching. Transmission electron microscopy measurements confirmed the formation of hollow carbon cages, and the absence of metal (oxide) nanoparticles suggested atomic dispersion of the metal species within the mesoporous carbon skeletons. X‐ray photoelectron spectroscopic analysis revealed a composition of mostly carbon, oxygen, and nitrogen, with ca. 1 % metals. Electrochemically, the dual‐metal ones showed a significant enhancement of the catalytic performance towards ORR in alkaline media, as compared to samples with single or no metal dopants. This was accounted for by the synergistic interaction between the Fe and Co centers in the carbon samples, as evidenced in X‐ray absorption spectroscopic studies. Remarkably, the CHS−FeCo sample exhibited apparent resistance against KSCN poisoning, where XPS analysis revealed oxidation of KSCN and no metal‐sulfur interaction, in sharp contrast to the Fe counterpart which was easily poisoned. Results from this study suggest that the synergistic interactions between dual metal centers may be exploited for enhanced ORR performance of carbon‐based nanocomposite catalysts.
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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".