Aging of<scp>high‐performance</scp>fibers used in firefighters' protective clothing: State of the knowledge and path forward
Bibliographic record
Abstract
Abstract High‐performance fibers developed since the 1960s have a wide range of applications including firefighters' protective clothing. Firefighters' protective clothing made of inherently flame‐resistant high‐performance fibers offers excellent protection in the new condition. However, these fibers experience aging as any polymer material. The situation is amplified due to the severe conditions associated with the firefighters' activities. And the consequences of a loss in the performance of the protective clothing can be dramatic for the firefighter's safety. This article provides a comprehensively review of the aging behavior of high‐performance fibers used in firefighters' protective clothing. Residual performance data have been identified both for used firefighter garments as well as fabric specimens subjected to accelerated aging. Research shows that different aging conditions affect the different fibers to a different degree. The specific conditions in which the aging is applied also affects the outcome in terms of loss in performance. Techniques successfully used to quantify the effect of aging on the performance of fire‐resistant fabrics are also briefly mentioned. Finally, the knowledge gained from this analysis of the literature as well as research gaps and further areas of investigation are discussed in this neglected yet critical topic of firefighters' protective clothing aging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".