Effects of high heat flux exposures on tensile strength of firefighters' protective clothing
Bibliographic record
Abstract
Summary The effects of thermal aging on tensile strength of three Kevlar/PBI blends used in the outer shell layer of firefighters' protective clothing were examined using cone calorimeter exposures of 10‐70 kW/m2 for 30‐300 seconds. Lower heat flux exposures of 10‐15 kW/m2 did not result in significant change in the tensile strength of the fabrics, but significant losses of tensile strength were first observed as the heat fluxes increased to 20‐30 kW/m2 for two ripstop fabrics, and 45 kW/m2 for the third fabric. Further decreases in tensile strength were observed with increasing heat flux until there was almost a total loss of original tensile strength following exposure to 70 kW/m2. Decreases in tensile strength were explained by comparing fabric temperature measurements made using an infrared thermometer, key temperatures in thermal gravimetric analysis test results, and the appearance of the three fabrics after the exposures. While duration of exposure had little effect on tensile strength after exposures to 10 kW/m2, tensile strength decreased with duration of exposure for heat fluxes of 20‐40 kW/m2. The effect of duration was greatest for a heat flux of 20 kW/m2. These results indicate that one must consider not only the maximum temperature that the fabric reaches, but the complete temperature‐time trace, including the time at which the fabric remains at the maximum temperature, when examining the effects of thermal aging on tensile strength.
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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) | 0.001 | 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) | 0.002 | 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".