Moisture barriers used in firefighters' protective clothing: Effect of accelerated hydrothermal aging on their mechanical and barrier performance
Bibliographic record
Abstract
Abstract Firefighters' protective clothing rely on moisture barriers to safeguard against liquid ingress while enabling sweat evaporation for comfort and safety. However, these moisture barriers can degrade over time, jeopardizing firefighters' safety. Existing evaluation methods, primarily visual inspection, are inadequate for assessing moisture barrier integrity in service. This study examines the effect of accelerated hydrothermal aging on the tear force, water vapor transmission rate (WVTR), and apparent contact angle in three moisture barrier models used in firefighter protective clothing. The moisture barriers studied varied in composition, structure, and base fabric fiber content. Results revealed that moisture barriers' responses to aging are influenced by factors, such as fabric structure, adhesive configuration, finish, and the presence of additional coatings. The hardening of the adhesive layer between the ePTFE membrane and the base fabric was observed in two of the moisture barriers, leading to a slight tear force reduction. Two of the moisture barriers also experienced crack and pit formation on the membrane side, which affected the WVTR. The water‐repellent finish on the fabric side degraded in one moisture barrier. Understanding these complex behaviors is essential for predicting the long‐term moisture barrier performance and enhancing firefighter safety.
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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".