Cyano‐Rich g‐C<sub>3</sub>N<sub>4</sub> in Photochemistry: Design, Applications, and Prospects
Bibliographic record
Abstract
Abstract Cyano‐rich g‐C3N4 materials are widely used in various fields of photochemistry due to the very powerful electron‐absorbing ability and electron storage function of cyano, as well as its advantages in improving light absorption, adjusting the energy band structure, increasing the polarization rate and electron density in the structure, active site concentration, and promoting oxygen activation ability. Notwithstanding, there is yet a huge knowledge break in the design, preparation, detection, application, and prospect of cyano‐rich g‐C3N4. Accordingly, an overall review is arranged to substantially comprehend the research progress and position of cyano‐rich g‐C3N4 materials. An overall overview of the current research position in the synthesis, characterization (determination of their location and quantity), application, and reaction mechanism analysis of cyano‐rich g‐C3N4 materials to provide a quantity of novel suggestions for cyano‐modified carbon nitride materials' construction is provided. In view of the prevailing challenges and outlooks of cyano‐rich g‐C3N4 materials, this paper will purify the growth direction of cyano‐rich g‐C3N4, to achieve a more in‐depth exploration and broaden the applications of cyano‐rich g‐C3N4.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".