Mechanosynthesis of a Structurally Characterized, Well‐Defined Graphitic Phosphorus‐Linked Carbon Nitride (g‐PCN) with Water Splitting Activity
Bibliographic record
Abstract
Abstract Graphitic phosphorus‐linked triazine networks (g‐PCNs) are an emergent class of carbon nitride materials that have attracted attention for their potential role in developing metal‐free water splitting photocatalysts, but remain difficult to access due to complicated synthetic procedures based on long reaction times, high‐temperature annealing (above 500 °C), and poor structural understanding. Here, a mild, lower temperature approach for the synthesis of catalytically active g‐PCN through combining a room‐temperature mechanochemical reaction of sodium phosphide and cyanuric chloride with only 1 h annealing at 300 °C is presented. Notably, this mechanosynthesized material is sufficiently ordered to permit unprecedented structural characterization of a g‐PCN layered solid by combining solid‐state magic angle spinning, nuclear magnetic resonance spectroscopy, X‐ray photoelectron spectroscopy, powder X‐ray diffraction (PXRD), and transmission electron microscopy, supported by dispersion‐corrected density functional theory modeling. The excellent match of experimental 31 P MAS NMR and PXRD data with modeling of the structure based on phosphorus‐linked triazine network layers makes the herein described mechanochemically synthesized material the first example of a photocatalytically active, as well as structurally characterized, g‐PCN.
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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".