Graphitic Carbon Nitride (g‐C<sub>3</sub>N<sub>4</sub>) Nanosheets as a Multipurpose Material for Detection of Amines and Solar‐Driven Hydrogen Production
Bibliographic record
Abstract
Abstract Graphitic carbon nitride nanosheets decorated by multiple functional groups (denoted as CN−KNa) were as a multipurpose material, both in photocatalytic applications and as a photo‐induced indicator. The presented g‐C 3 N 4 is fabricated via a facile alkali‐salt‐assisted calcination method. The structural analysis reveals significant changes in the structure of the host CN−KNa nanosheets associated with the existence of multiple functional groups (for example, hydroxy, carbonyl, and cyano groups). Such modifications lead to enhanced light absorption and charge separation, resulting in an efficient photocatalyst not only for solar‐driven hydrogen production but also for primary amine detection in aqueous solution. Thus, the solar light driven photocatalytic hydrogen evolution yield using the synthesized CN−KNa sample is found to be 50.3 μmol h −1 , which is approximately 14 times higher than that of bulk g‐C 3 N 4 . More importantly, this functional‐group‐decorated CN−KNa facilitated electron transportation between CN−KNa and amine compounds, causing a colour change in the solution mixture, which has been observed for the first time. This novel observation indicates that CN−KNa can be considered a new class of photo‐induced indicator agent to detect primary amine compounds in aqueous solution.
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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) | 0.000 | 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".