Preparation of Graphene Nanoplatelets by Thermal Shock Combined with Ball Milling Methods for Fabricating Flame-Retardant Polymers
Bibliographic record
Abstract
Graphene nanoplatelets were successfully prepared from graphite powder by simple and scalable thermal shock combined with ball milling methods. The formation of the graphene nanoplatelets were observed by field-emission scanning electron microscopes and Brunauer–Emmett–Teller methods with the much smaller number of layers and the considerable increase of specific surface area in comparison to the initial expanded graphite material. The other characterizations such as Fourier transform infrared spectroscopy and X-ray powder diffraction methods of graphene nanoplatelets showed unchanged structure. These graphene nanoplatelets were combined with aluminum trihydroxide and zinc borate to prepare flame-retardant polycarbonate plastic and chlorine-sulfonated polyethylene rubber. The prepared composites showed the improvement of flame resistance properties with V0 level according to the UL-94 test method, and the limiting oxygen index value was higher than 27.
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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 | 0.001 | 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 | 0.001 |
| 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".