Genuine Active Species Generated from Fe<sub>3</sub>N Nanotube by Synergistic CoNi Doping for Boosted Oxygen Evolution Catalysis
Bibliographic record
Abstract
Abstract The surface reconstruction of oxygen evolution reaction (OER) catalysts has been proven favorable for enhancing its catalytic activity. However, what is the active site and how to promote the active species generation remain unclear and are still under debate. Here, the in situ synthesis of CoNi incorporated Fe 3 N nanotubes (CoNi–Fe 3 N) on the iron foil through the anodization/electrodeposition/nitridation process for use of boosted OER catalysis is reported. The synergistic CoNi doping induces the lattice expansion and up shifts the d‐band center of Fe 3 N, which enhances the adsorption of hydroxyl groups from electrolyte during the OER catalysis, facilitating the generation of active CoNi–FeOOH on the Fe 3 N nanotube surface. As a result of this OER‐conditioned surface reconstruction, the optimized catalyst requires an overpotential of only 285 mV at a current density of 10 mA cm −2 with a Tafel slope of 34 mV dec −1 , outperforming commercial RuO 2 catalysts. Density functional theory (DFT) calculations further reveal that the Ni site in CoNi–FeOOH modulates the adsorption of OER intermediates and delivers a lower overpotential than those from Fe and Co sites, serving as the optimal active site for excellent OER performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".