Accelerated Zero-Stress Hydrothermal Aging of Dry E-Glass Fibers and Service Life Prediction Using Arrhenius Model
Bibliographic record
Abstract
Comprehending the degradation of glass fibers is crucial for service applications involving dry and wet conditions, especially when prolonged contact with water above room temperature is present. Depending on the polymer material, both thermosetting and thermoplastic matrices can permit the ingress of moisture. Therefore, fiber reinforcements embedded in the polymer matrix may experience moisture exposure. Additionally, some structural applications use fiber devoid of any matrix (dry fibers), in which water exposure must be avoided. In all of these cases, moisture may, therefore, have a significant impact on the reinforcing elements and the rate of degradation. The present work focuses on the effects of hydrothermal aging on the mechanical durability of long E-glass fibers by immersion in water at 60 °C, 71 °C, and 82 °C. A service life forecast model was created utilizing the Arrhenius technique, and a master curve of strength variation with exposure time was created for E-glass fibers at 60 °C. Using this modeling approach, it is possible to approximate the amount of time it will take to attain a given degradation level over a specified range of temperatures. Scanning electron microscopy was used to evaluate morphological changes in fiber surfaces due to hydrothermal exposure, while Fourier transform infrared spectroscopy and mass dissolution studies were used to elucidate the mechanism of the strength loss.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".