Novel sustainable biobased flame retardant from functionalized vegetable oil for enhanced flame retardancy of engineering plastic
Bibliographic record
Abstract
The flame retardancy of an engineering plastic, poly(butylene terephthalate) (PBT), with a biobased flame retardant (FR) made from phosphorylated linseed oil (PLO) and phosphorylated downstream corn oil (PCO) was studied. Different phosphorus moieties were incorporated into the vegetable oil backbone through a ring-opening reaction. The chemical structure of the phosphorylated oil was confirmed by Fourier-transform infrared (FTIR) and nuclear resonance magnetic (NMR) spectroscopy. It was found that the incorporation of only 7.5 wt% of PLO was sufficient to change the UL-94 fire class of PBT from non-rating to V-0. The flame-retardancy mechanism of the PBT/PLO blends was evaluated from TGA-FTIR analysis. The combined effects of the gas phase mechanism and the dripping tendency of the blends aided to retard the flame propagation effectively. As the synthesized PLO and PCO contained high free fatty acids, the acid-ester exchange reaction occurred in the blends to form oligomers during the ignition. As a result, the blend dripped immediately and the drips carried all the heat to prevent fire. This work suggests that this sustainable biobased FR could be a desirable alternative to halogen-based FRs for PBT and other engineering polymers to develop more environmentally friendly FR products for various future applications.
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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".