Hollow porous prismatic graphitic carbon nitride with nitrogen vacancies and oxygen doping: a high-performance visible light-driven catalyst for nitrogen fixation
Bibliographic record
Abstract
Hollow porous prismatic graphitic carbon nitride with nitrogen vacancies and oxygen doping was successfully constructed using dicyandiamidine as the only raw material via a facile two-step strategy of a low-temperature hydrothermal method followed by a subsequent calcination process. The as-obtained graphitic carbon nitride showed a hollow prismatic morphology with loose spongy-like walls, a hierarchical pore structure, and a specific surface area of 220.16 m2 g-1. Such graphitic carbon nitride exhibited an ultrahigh nitrogen fixation rate of 118.8 mg L-1 h-1 gcat-1 under visible light irradiation and showed excellent stability during the reactions. A possible mechanism for photocatalytic nitrogen fixation on the catalyst was proposed as follows: under visible-light irradiation, graphitic carbon nitride with nitrogen vacancies and oxygen doping underwent charge separation to generate electron-hole pairs, and then the photogenerated electrons on the conduction band were quickly transferred to the nitrogen vacancy induced mid-gap state; consequently, the trapped electrons reacted with the activated nitrogen on the nitrogen vacancies to produce ammonia. The significant enhancement in the photocatalytic nitrogen fixation performance of graphitic carbon nitride can be attributed to its unique hollow prismatic morphology with a loose porous structure, fully exposed active sites of nitrogen vacancies, more negative conduction band, suitable visible-light response and the efficient separation of photogenerated electron-hole pairs.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".