Effect of Different Laundering and Drying Procedures on the Performance of Fire‐Protective Fabrics
Bibliographic record
Abstract
ABSTRACT Fire‐protective fabrics play a critical role in keeping firefighters and other workers exposed to heat and flame hazards safe. However, some fibers used in these fabrics are sensitive to laundering, which may lead to significant reductions in their performance. This study analyzes the effect of laundering procedures using different types of laboratory washing equipment, washing temperatures, and drying methods on the mechanical strength and water repellency of five commercial fabrics with different fiber contents used as outer shell in firefighter protective clothing. The fabrics were also subjected to repeated launderings performed in a commercial facility. It was observed that accelerated laundering according to the AATCC TM61 test method did not correctly simulate the effect produced by repeated commercial laundering. On the other hand, domestic laundering using a front‐loading machine and flat drying led to a similar reduction in strength in the fabrics as commercial laundering performed at the same temperature of 40°C (69%–90% strength reduction after 50 laundering cycles depending on the fabric). Both procedures resulted in fibrillation at the surface of the fabrics, which was attributed to the spinning step. However, no laboratory protocol was able to create the complete loss in water repellency observed after 50 cycles of commercial laundering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".