Mechanochemical synthesis of structurally well-defined graphitic phosphorus-linked carbon nitride (g-PCN) with water splitting activity
Bibliographic record
Abstract
Heteroatom-doped carbon nitride (CN) materials have shown much potential as metal-free photocatalysts for water splitting. Graphitic phosphorus-linked triazine network (g-PCN) materials are a unique class within this family of materials, but remain difficult to access due to long reaction times annealing at temperatures above 500 °C and often afford ill-understood structures. Here, we reveal a milder, lower temperature approach for the synthesis of catalytically active g-PCN materials through combining a room-temperature mechanochemical reaction of sodium phosphide and cyanuric chloride with brief (1 hour) annealing of the milled material at 300 °C. This rapid, low temperature procedure yields ordered g-PCN catalysts whose layered structure was determined through a combination of magic-angle spinning nuclear magnetic resonance (MAS NMR), X-ray photoelectron spectroscopy (XPS), powder X-ray diffraction (PXRD) and transmission electron microscopy (TEM). An excellent level of accuracy to simulated 31P MAS NMR signals and PXRD patterns were obtained for the structure of the synthesized layered phosphorus-linked triazine networks following dispersion-corrected density functional theory (DFT). The mechanochemically-generated g-PCN is a highly effective photocatalyst for the hydrogen evolution reaction, producing 122 µmol H2 h-1 g-1 under broad spectrum irradiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".