Hydrothermal aging behavior of high‐performance polymeric fibers: Mechanical performance at the yarn scale and chemical analysis
Bibliographic record
Abstract
Abstract High‐performance fibers are used in fire‐protective garments due to their exceptional thermal stability and mechanical performance. However, these garments suffer from a reduction in their performance over their lifetime. The purpose of this study was to investigate the hydrothermal aging of 15 yarns contained in eight fabrics made of different fiber blends. The accelerated hydrothermal aging was performed via immersion in reverse osmosis (RO) and acidic water at temperatures between 40 °C and 90 °C for up to 1200 h. The resulting mechanical, chemical, and physicochemical changes in the yarns and fabrics were assessed. The result showed a large drop in the breaking force of yarns made from para‐aramid/polybenzimidazole (PBI) fiber blends in all aging water conditions. For the other fabrics, aging in acidic water and a jar with the PBI‐containing fabrics generally caused a larger decrease in strength compared to aging in RO water in a separate jar. The results also showed that a change in crystallinity rather than in chemical structure appeared to be the cause for the changes in tensile strength after hydrothermal aging. The findings of this study will contribute to identifying strategies to improve the long‐term performance of fire‐protective fabrics.
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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 | 0.000 |
| 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".