Thermal aging of high‐performance fabrics used in the outer shell of firefighters' protective clothing
Bibliographic record
Abstract
Abstract High‐performance fibers are known for their exceptional specific strength and resistance to various hazardous conditions, including fire. However, the long‐term performance of these fibers when exposed to convective heat has rarely been reported. This study investigated the accelerated thermal aging behavior of three high‐performance fabrics of different blends of inherently flame‐resistant high‐performance fibers: copolymer of aramids (Technora®)/polybenzoxazole (PBO); para‐aramid/meta‐aramid; and para‐aramid/polybenzimidazole (PBI). Fabric specimens were thermally aged for up to 1200 h at temperatures ranging from 90 to 320°C. While all three fabrics experienced losses in the breaking force, the Technora®/PBO fabric displayed the best strength retention, despite the complete disappearance of the Technora® fiber crystallinity after aging at 320°C for 1200 h. The para‐aramid/PBI blended fabric showed signs of competing aging processes at high temperatures. An increase in the fabrics' crystallinity and evidence of chain scission were observed after thermal aging. Additionally, degradation in the fabric's water‐repellent finish was observed. The findings of this study will contribute to the development of more durable and safer protective gear, particularly for high‐risk activities like firefighting.
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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".