Hexachlorocyclotriphosphazene Functionalized Graphene Oxide as a Highly Efficient Flame Retardant
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A flame-retardant composite was synthesized through a simple graphene oxide functionalization route with hexachlorocyclotriphosphazene and p -phenylenediamine. Flame experiments conducted on the synthesized composite proved its importance as tremendously resistant to fire. The thermogravimetric analysis (TGA) shows clearly that the functionalized graphene oxide (FGO) exhibits an enhanced thermal stability and better temperature resistance. A thermoset epoxy resin was prepared by incorporating different percentages (2, 5, and 10%) of FGO to diglycidyl ether of bisphenol A (DGEBA). The flame-retardant properties, thermal degradation behavior, and combustion of the DGEBA thermosets cured by m -phenylenediamine were investigated using a Bunsen burner flame approaching the flame temperature of a fire and TGA. The chemical structure of FGO was characterized with spectroscopic and imaging techniques including Fourier transform infrared spectroscopy, Raman spectroscopy, X-ray diffraction, TGA, scanning electron microscopy, energy-dispersive X-ray spectroscopy elemental mapping, and X-ray photoelectron spectroscopy. Due to its high flame-retardant capabilities, such a composite could promise potential applications in the manufacture of inflammable materials for different uses.
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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".