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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".