Moisture barriers used in firefighters' protective clothing: Effect of accelerated ultraviolet radiation aging on their mechanical and barrier performance
Bibliographic record
Abstract
Abstract Firefighters rely on their protective clothing as a second skin to perform their job. In the middle of this multilayered protective garment, the moisture barrier plays a vital role in preventing liquids to enter while allowing perspiration to escape. This research delves into the impact of accelerated ultraviolet (UV) aging on the performance of firefighter protective clothing moisture barriers, focusing on tear force retention, water vapor transmission rate (WVTR), and wetting via apparent contact angle. Changes in the tearing behavior and reductions in the tear force and tearing distance were observed after aging. Additionally, a reduction in WVTR was found for all the moisture barriers, due to pore closure in the ePTFE/FR PU membrane. In terms of water repellency, the base fabric side of one moisture barrier experienced a transition from hydrophobic to superhydrophilic potentially due to the degradation of the water‐repellent finish. The investigation also revealed that, due to the screening effect by the outer shell fabric, only a low percentage of UV radiation received by the turnout gear, less than 2% in the UVA range, may reach the moisture barrier. These results indicate the need to consider realistic exposure scenarios when assessing moisture barrier service life.
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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) | 0.000 | 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".