Hydrothermal Aging and Humidity Exposure of Carbon and Basalt Fibers and Life Time Prediction
Bibliographic record
Abstract
Fibers as a reinforcement in polymer-based composite materials play an essential role in the composites’ mechanical performance. It is, therefore, crucial to understand how fibers are affected by different environmental conditions, such as water exposure at elevated temperatures. Even when embedded in a matrix material, i.e., a thermoset or thermosetting polymer, exposure to moisture may occur. Therefore, in many structural applications of fiber-reinforced polymer composites, moisture may 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 carbon and basalt fibers by immersion in tap water at 60 °C, 71 °C, and 82 °C. A service life prediction model based on the Arrhenius technique was explored. Using this model, it is possible to forecast the amount of time that it takes to attain a given degradation level over a specified range of temperatures. The present study also investigated changes in tensile strength in response to exposure to 90% humidity at 90 °C. In addition, the chemical elements released during aging in water were determined. Fourier-transform infrared spectroscopy and mass dissolution studies were conducted to elucidate the mechanism causing strength losses. Scanning electron microscopy was employed to evaluate changes of the fiber surface morphologies due to hydrothermal exposure.
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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) | 0.000 | 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".