Thermo-oxidative stability and flammability properties of bamboo/kenaf/nanoclay/epoxy hybrid nanocomposites
Bibliographic record
Abstract
In this study, three types of nanoclay [halloysite nanotube (HNT), montmorillonite (MMT) and organically modified MMT (OMMT)] were incorporated into bamboo/kenaf (B/K) reinforced epoxy hybrid composites to compare their thermo-oxidative (TOD) stability and flammability properties. B/K (50 : 50) hybrid nanocomposites were fabricated by adding 1% loading (by weight) nanoclay through a hand lay-up technique. Wide angle X-ray scattering (WAXS) and field emission scanning electron microscopy (FESEM) were used to study the morphology of the nanoclay-epoxy mixture. The TOD stability of the hybrid nanocomposites was studied with a thermogravimetry analyzer (TGA) under oxygen atmosphere. The flammability properties were evaluated using the Underwriters Laboratories 94 horizontal burning test (UL-94HB), limiting oxygen index (LOI), cone calorimetry and smoke density test. The morphological study reveals that MMT/epoxy and HNT/epoxy are highly agglomerated while OMMT/epoxy reveals a more uniform distribution morphology. The obtained results reveal that B/K/HNT shows better TOD stability below 300 °C, but B/K/MMT and B/K/OMMT show high residue content and decomposition temperatures above 300 °C. The flame retardancy of the hybrid nanocomposites improved with the loading of all types of nanoclay, but B/K/OMMT shows higher flame retardancy than B/K/MMT and B/K/HNT hybrid nanocomposites. Hybrid nanocomposites show improvement in flame properties in terms of peak heat release rate (pHRR), total heat release, fire growth rate index (FIGRA) and maximum average rate of heat emission (MARHE) and smoke growth rate index (SMOGRA) indicators. The findings from this work can be utilized to prepare high-performance fire retardant natural fiber reinforced epoxy hybrid composites for automotive and construction applications to save human lives.
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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".