New Flame Retardant and Antimicrobial Paints Based on Epoxy Paint Incorporated by Hexachlorocylodiphosphazane Derivatives for Protective Coating
Bibliographic record
Abstract
Flame retardants can be incorporated into polymeric material either as additives or as reactive materials. Additive type flame retardants are widely used by means of blending them with a specific polymeric material. In this particular research, hexachlorocylodiphosphazane derivatives type (I-II) were synthesized for use as flame retardant and antimicrobial additives with epoxy varnish. These additives are physically incorporated into the epoxy varnish formula. Experimental coatings were manufactured on a laboratory scale and applied by brush on wood and steel panels. The fire retardant ability of each coating type was characterized using the limiting oxygen index (LOI) test. The mechanical properties of these flame retardants were also examined to evaluate the drawbacks of the additives. Results of the LOI indicated that coating with these compounds containing chlorine, nitrogen and phosphorus exhibit a very good retardant effect, when blended with epoxy varnish comparing with the blanket sample which not contain on the hexachlorocylodiphosphazane derivative as a additives. The hexachlorocylodiphosphazane derivative also exhibit mild results as preservative against microbiological attack. The mechanical properties of the painted dry films were investigated acordinting to ASTM.
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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".