A Comprehensive Review on the Boosted Effects of Anion Vacancies in Photocatalytic Solar Water Splitting: Focus on g-C<sub>3</sub>N<sub>4</sub> with Carbon and Nitrogen Vacancies
Bibliographic record
Abstract
As one of the best sustainable approaches for visible-light production of hydrogen (H 2 ) to meet energy demands, semiconductor-based photocatalysis has received broad interest in recent decades. The fundamental restrictions of graphitic carbon nitride (g-C 3 N 4 ) as a promising metal-free photocatalytic semiconductor in water splitting, like insufficient visible-light harvesting and high electron–hole (e/h) pairs recombination, have limited its applications for this goal. In this regard, the optical, charge separation, and surface features of g-C 3 N 4 can be tuned via engineering C/N vacancies, which is reviewed here for water splitting by g-C 3 N 4 . Reports confirm that the enhanced surface features of g-C 3 N 4 resulting from the engineering of C/N vacancies are helpful for water adsorption on its surface, improving the water-splitting kinetics. First in this Review, recent improvements to the structural and optical characteristics of g-C 3 N 4 by introducing C/N vacancies, especially nitrogen vacancies, will be discussed to illustrate its better photocatalytic performance. Then, various strategies for creating and controlling C/N vacancies will be reviewed. The critical roles of C/N vacancies for optimizing photocatalytic performance will also be described, and finally advances in defective photocatalysis water oxidation will be addressed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".