Environmentally friendly fire retardant natural fibre composites: A review
Bibliographic record
Abstract
There is a growing drive in replacing conventional non-renewable fibres such as glass and carbon reinforced composites with more sustainable and renewable reinforcements such as flax, hemp, jute in biobased composites in key industry sectors such as automotive, marine, building and construction motivated by lower carbon footprint and sustainability. Despite this drive, flammability characteristics of these sustainable biobased composites are not fully understood. Through an up-to-date review, this article meticulously discusses the theme of bio-based and eco-friendly flame retardants (FRs), delving into their intricate mechanisms, flammability testing methodologies, and emerging research trends. It underscores the pivotal necessity of developing tailor-made eco-friendly FRs customised for a diverse range of materials to bolster fire safety in sustainable material applications. Furthermore, it illuminates the limitations associated with prevalent methods for assessing flammability and advocates for advancements in dynamic testing and multi-scale analyses to more accurately simulate real-world fire scenarios. This review also highlights the key characteristics of natural plant fibres and their composites critical for the development of sustainable and fire safe materials for key applications areas. Additionally, it highlights the diverse spectrum of strategies employed in the realm of flame-retardant materials research, emphasizing a pronounced shift towards eco-friendly alternatives, innovative coatings, and the ongoing exploration of synthetic biopolymers, nanocomposites, and fibres in the pursuit of heightened fire safety. Amidst the inherent challenges, this comprehensive review unequivocally underscores the pivotal role of interdisciplinary research collaboration in driving forward fire safety within the domain of sustainable materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".