Mechanical Performance of Aged Long Fibers: Direct Water Exposure and Temperature Effect
Bibliographic record
Abstract
Abstract Long fiber-reinforced composite materials consist of continuous fibers with high strength and modulus embedded in either a thermoset or thermoplastic matrix. The resulting composite material provides a combination of properties that cannot be achieved with either of the constituents acting alone. In composite structures, fibers are the primary load-carrying element, whereas the matrix transfers stress to and between the fibers while protecting them from adverse environmental conditions and mechanical damages. While thermosetting matrices provide a high level of protection against water permeation, exposure to moisture may still be significant in certain thermoplastics. Therefore, the effect of moisture on the reinforcing elements and the degradation rate may be considerable. The presented study investigated the effects of environmental aging conditions on different commercially available continuous fibers, i.e., glass fiber, carbon fiber, and basalt fiber. The fibers were soaked in water at room and elevated temperature to investigate the degradation mechanisms and, ultimately, the mechanical performance of the fibers. Mechanical testing was performed with wet fibers and dried fibers after aging. In addition, scanning electron microscopy was employed to explore the responsible mechanism for fiber degradation by environmental aging.
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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".