Extract from the peels of jackfruit ( <i>Artocarpus heterophyllus</i> ): Flame retardancy and toxic gaseous emission suppression effects on cotton textiles
Bibliographic record
Abstract
Abstract This paper reports on the application of extract from the peels of jackfruit ( Artocarpus heterophyllus Lam.) to increase flame retardancy and reduce toxic gaseous emissions from the combustion of cotton textiles. In particular, the results from Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) of treated and untreated samples proved the incorporation of jackfruit peels extract onto the textile substrate, while the results from thermogravimetric analysis (TGA) and standard flammability tests of treated and untreated samples, and their corresponding limiting oxygen index (LOI), proving the flame retardancy effect of jackfruit peels extract on the textile substrate. The textile substrate treated with jackfruit peels extract exhibited excellent flame retardancy, evident by LOI value increasing to 26.8% and samples self‐extinguishing within 4.5 s after being removed from the reference flame. In addition, the increase in flame retardancy of treated textile samples also demonstrated good washing durability, even after 30 cycles of standard washing. More importantly, gas chromatography coupled mass spectroscopy (GC/MS) analysis of gaseous emissions from the combustion of samples treated by jackfruit peels extract and by commercial Pyrovatex CP suggested that the use of jackfruit peels extract could help greatly reduce the release of toxic volatile substances, which would pose significant risk to the health of human and the ability of people to safely evacuate from fire accidents. In conclusion, these results have demonstrated the potential of a novel green approach for the fabrication of flame‐retardant textiles.
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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".