Recent progress in flame retardancy of graphene and bio-based sustainable flame retardants for polymer composite applications
Bibliographic record
Abstract
To address the restricted application of halogenated flame retardants (FRs), both industrial and academic sectors have endeavored to produce environmentally friendly, effective, and low-toxicity flame retardants for polymers. Bio-based FRs have attracted considerable interest due to their cost-effectiveness, widespread availability, and accessibility. Conversely, nanostructured graphene-based sustainable FRs provide further advantages to polymer composites beyond fire prevention, such as enhanced resistance to degradation, increased thermal stability, mechanical strength and extended lifespan. This review aims to provide a comprehensive summary of the flame retardancy characteristics of polymers and their composites with newly developed bio-based and graphene-based sustainable FRs. The flame-retardant properties, mechanism, and synergistic effects of the recently developed graphene and bio-based (lignin, phytic acid, chitosan, tannic acid, polydopamine, vegetable oil, biocarbon and keratinous fiber) polymer composites are thoroughly discussed in this article. Graphene-based FRs enhance polymer flame resistance by dissipating heat, forming protective barriers, and promoting char formation, reducing heat and gas transfer. Similarly, nitrogen- and phosphorus-rich bio-based FRs improve fire safety by forming dense char layers that block heat and suppress flammable gas release. The superior flame retardancy of these FR-loaded polymer composites allows for their application across various industry sectors, including automotive, aerospace, electronics, military, and construction. However, challenges such as compatibility between the polymer matrix and FRs, expensive and complicated fabrication processes, limitations of raw material supplies and industrial scalability need to be further researched. In conclusion, these FRs offer a promising path toward safer, more effective, per- and polyfluoroalkyl substances (PFAS)-free and more sustainable flame-resistant polymer composites in key industrial sectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".