Defect‐Rich Nitrogen Doped Co<sub>3</sub>O<sub>4</sub>/C Porous Nanocubes Enable High‐Efficiency Bifunctional Oxygen Electrocatalysis
Bibliographic record
Abstract
Abstract Heteroatom doping plays a significant role in optimizing the catalytic performance of electrocatalysts. However, research on heteroatom doped electrocatalysts with abundant defects and well‐defined morphology remain a great challenge. Herein, a class of defect‐engineered nitrogen‐doped Co 3 O 4 nanoparticles/nitrogen‐doped carbon framework (N‐Co 3 O 4 @NC) strongly coupled porous nanocubes, made using a zeolitic imidazolate framework‐67 via a controllable N‐doping strategy, is demonstrated for achieving remarkable oxygen evolution reaction (OER) catalysis. X‐ray photoelectron spectroscopy, X‐ray absorption fine structure, and electron spin resonance results clearly reveal the formation of a considerable amount of nitrogen dopants and oxygen vacancies in N‐Co 3 O 4 @NC. The defect engineering of N‐Co 3 O 4 @NC makes it exhibit an overpotential of only 266 mV to reach 10 mA cm −2 , a low Tafel slope of 54.9 mV dec −1 and superior catalytic stability for OER, which is comparable to that of commercial RuO 2 . Density functional theory calculations indicate N‐doping could promote catalytic activity via improving electronic conductivity, accelerating reaction kinetics, and optimizing the adsorption energy for intermediates of OER. Interestingly, N‐Co 3 O 4 @NC also shows a superior oxygen reduction reaction activity, making it a bifunctional electrocatalyst for zinc–air batteries. The zinc–air battery with the N‐Co 3 O 4 @NC cathode demonstrates superior efficiency and durability, showing the feasibility of N‐Co 3 O 4 /NC in electrochemical energy devices.
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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".