Selective Fragmentation through C–N Bond Cleavage of Carbon Nitride Framework for Enhanced Photocatalytic Hydrogen Production
Bibliographic record
Abstract
A simple, practical approach for the structural modification of bulk g-C 3 N 4 employing high-pressure NH 3 and H 2 O formed by the polycondensation of urea is reported. The high-pressure processes the planarization of carbon nitride sheets that is disruptive because of structural distortion or defects, thus creating non-crystalline lines with highly reactive carbon species. The reaction of these carbon species with NH 3 leads to highly selective and oriented fragmentation of the carbon nitride framework, which is entirely different from previous reports, producing nanofragments with a very small density of defects. The high pressure proceeds the sheet planarization and the structural condensation of nanofragments, resulting in very high crystallinity. The fragmentation also creating a high concentration of functional groups (−NH 2 and −OH) on the edge of C 3 N 4 sheets with a suitable proportion, constructing a large optimized hydrogen-bond network across intra- and interplanes that further enhance the crystallinity of the formed nanofragments. The high crystallinity, especially the strong planarization of carbon nitride sheets, significantly speeds up the charge separation and transfer, while the functional groups on the edge of sheets result in an excellent charge drive. Also, these groups simultaneously shift the conduction band to a higher level and improve proton adsorption and activation. As such, the as-prepared nanofragment photocatalyst exhibits a photocatalytic hydrogen production rate that is nearly five times increased, as compared to that of the bulk g-C 3 N 4, with a high quantum efficiency of 12.3% at 420 nm.
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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) | 0.001 | 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".