Effect of Meltable Triazine-DOPO Additive on Rheological, Mechanical, and Flammability Properties of PA6
Bibliographic record
Abstract
Through a straightforward approach, a new meltable, halogen-free, nitrogen-phosphorus-based flame retardant (FR), 6-(2-(4,6-diamino-1,3,5-triazin-2-yl)ethyl) dibenzo[c,e][1,2]oxaphosphinine 6-oxide (DTE-DOPO) was synthesized and incorporated in polyamide 6 (PA6). It was proved that a very low phosphorus content of 1.46 wt% for DTE-DOPO additive improved the flame retardancy of PA6, leading to a non-flammable material. The performance of the new additive was compared to that of the commercially-available Exolit® OP 1230. The PA6 formulations were evaluated by measuring the rheological, mechanical, and flammability behavior. Using compounding by melt extrusion, 17 wt% additives was introduced into PA6 matrix and the corresponding formulations were characterized. The results evidenced a higher homogeneity of DTE-DOPO with PA6, a high thermal stability with a catalyzing decomposition effect on PA6 caused by the presence of the new developed FR, enhanced elasticity for the PA6/DTE-DOPO formulation and a V0 rating for both formulations. Thermal and fire analysis indicated a primary gas-phase activity, combined with a complete suppression of the self-sustained burning for the PA6/DTE-DOPO formulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".