Highly tough and flame retardant polystyrene composites by elastomeric nanofibers and hexagonal boron nitride
Bibliographic record
Abstract
Thermoplastics flammability remains a considerable threat during fire incidents. Conventionally, halogen-free fire retardant (FR) additives are incorporated into thermoplastics to reduce fire hazards. However, the incorporation of FR additives compromises the mechanical properties (most notably, toughness) of thermoplastics, which has impeded the development of thermoplastic products that possess both high mechanical and fire retarding performances. This study reports an in situ nano-fibrillation strategy to fabricate thermoplastics that exhibit fire retarding properties and a combination of high stiffness and toughness. The proposed composites were composed of in situ thermoplastic polyester elastomer (SBC) nanofibers within a polystyrene (PS) matrix containing hexagonal boron nitride (hBN) as the FR additive. The presence of elastomeric nanofibers successfully mitigated the losses in mechanical performances caused by the incorporation of 2 wt% hBN. Specifically, the inclusion of 15 wt% SBC nanofibers significantly enhanced the toughness of the PS-hBN composite by 350% with negligible effects on the stiffness as compared to neat PS. Furthermore, the presence of nanofibers resulted in synergies with hBN to fabricate composites with enhanced fire retarding performance since the total heat release (THR) of PS-hBN composite decreased from 212 to 189 MJ m−2 with 10 wt% nanofibers. Thus, nanofibers behave as a multifunctional component that compensated for the losses in mechanical performances caused by hBN incorporation, while enhancing the fire retarding performance. This strategy can be effectively implemented to fabricate the next generation of polymer composites with high fire retarding and mechanical properties for various applications including energy storage packs for batteries and electronics.
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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) | 0.000 | 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".