The effectiveness of magnesium carbonate‐based flame retardants for poly(ethylene‐co‐vinyl acetate) and poly(ethylene‐co‐ethyl acrylate)
Bibliographic record
Abstract
Abstract In this report we outline recent work on the evaluation of magnesium carbonate‐based flame retardants for polymers commonly used in halogen‐free flame retardant wire and cable applications: poly(ethylene‐co‐vinyl acetate) (EVA) and poly(ethylene‐co‐ethyl acrylate) (EEA). Natural magnesium carbonate (magnesite), synthetic magnesium carbonate (hydromagnesite), and hydromagnesite/huntite blends were combined with EVA or EEA and tested for flame retardancy effectiveness with the cone calorimeter. The flammability results showed that the effectiveness of these carbonates was polymer dependent, suggesting that polymer degradation chemistry played a role in the flammability reduction mechanism. Hydromagnesites were, in general, more effective in reducing flammability, being comparable in performance to magnesium hydroxide. Finally, we report some polymer–clay (organically treated montmorillonite and magadiite) + magnesium carbonate flame retardant results which showed that the nanocomposite yielded mixed results. Specifically, the polymer–clay nanocomposite samples did not always yield the greatest reductions in peak heat release rate. Copyright © 2007 John Wiley & Sons, Ltd.
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".