Engineering Fe-N4 Electronic Structure with Adjacent Co-N2C2 and Co Nanoclusters on Carbon Nanotubes for Efficient Oxygen Electrocatalysis
Bibliographic record
Abstract
Abstract Regulating the local configuration of atomically dispersed transition-metal atom catalysts is the key to oxygen electrocatalysis performance enhancement. Unlike the previously reported single-atom or dual-atom configurations, we designed a new type of binary-atom catalyst, through engineering Fe-N4 electronic structure with adjacent Co-N2C2 and nitrogen-coordinated Co nanoclusters, as oxygen electrocatalysts. The resultant optimized electronic structure of the Fe-N4 active center favors the binding capability of intermediates and enhances oxygen reduction reaction (ORR) activity in both alkaline and acid conditions. In addition, anchoring M–N–C atomic sites on highly graphitized carbon supports guarantees of efficient charge- and mass-transports, and escorts the high bifunctional catalytic activity of the entire catalyst. Further, through the combination of electrochemical studies and in-situ X-ray absorption spectroscopy analyses, the ORR degradation mechanisms under highly oxidative conditions during oxygen evolution reaction processes were revealed. This work developed a new binary-atom catalyst and systematically investigates the effect of highly oxidative environments on ORR electrochemical behavior. It demonstrates the strategy for facilitating oxygen electrocatalytic activity and stability of the atomically dispersed M–N–C 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) | 0.001 | 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".