Thermal Conversion of Acetylene‐Containing Cyclotriphosphazene to Graphitic Materials: Controlling Solid‐State Morphology through Heating Rates
Bibliographic record
Abstract
Abstract Carbon‐rich materials have growing potential for applications ranging from electronics to drug delivery. Traditional methods for preparing these materials often require high temperatures and yield mixtures of products with poor control over structure and properties. To address this, researchers are increasingly using molecular precursors with specific reactive sites that allow for a tunable and well‐defined synthesis. This work presents alkyne‐terminated cyclotriphosphazenes as promising precursors for synthesizing nitrogen and phosphorus rich graphitic materials with tunable solid‐state properties. By applying thermal annealing below 400 °C, it was demonstrated by our groups that precise heating can selectively control ring‐opening polymerization of the phosphazene core and crosslinking of terminal acetylenes. Spectroscopic and thermal analysis revealed that slow thermal heating (below 32 °C/min) promotes simultaneous ring‐opening polymerization, acetylene crosslinking and graphitization to yield a brittle thin film. In contrast, rapid heating (above 32 °C/min) exclusively induces acetylene crosslinking and graphitization, preserving the cyclotriphosphazene ring and producing a soluble, amorphous black powder. Characterization by electron microscopy and gas absorption analysis confirmed that the fast‐heated material has a surface area of 261.03 m2/g, a nitrogen uptake of 822.50 cm3/g, and a significant increase in pore volume. These findings present a new versatile approach for generating carbon‐rich, porous graphitic materials for various applications.
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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".