Oxygen‐Deficient FeNbO<sub>4‐x</sub> In‐Situ Growth in Honey‐Derived N‐Doping Porous Carbon for Overall Water Splitting
Bibliographic record
Abstract
Abstract It is still a great challenge to reasonably design green, low cost, high activity and good stability catalysts for overall water splitting (OWS). Here, we introduce a novel catalyst with ferric niobate (FeNbO 4 ) in‐situ growing in honey‐derived porous carbon of high specific surface area, and its catalytic activity is further enhanced by micro‐regulation (oxygen vacancy and N‐doping). From the experimental results and density functional theory (DFT) calculations, the oxygen vacancy in catalyst FeNbO 4‐x @NC regulates the local charge density of active site, thus increasing conductivity and optimizing hydrogen/oxygen species adsorption energy. FeNbO 4 in‐situ grows within N‐doping honey‐derived porous carbon, which can enhance active specific surface area exposure, strengthen gaseous substances escape rate, and accelerate electrons/ions transfer and electrolytes diffusion. Moreover, in‐situ Raman also confirms O‐species generation in oxygen evolution reaction (OER). As a result, the catalyst FeNbO 4‐x @NC shows good electrochemical performance in OER, HER and OWS.
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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".