Effects of resin types on the durability of single yarn polymer composites exposed to hygrothermal environment
Bibliographic record
Abstract
This study evaluated the durability of glass fibre composites prepared using epoxy, bio-epoxy, and vinyl ester resin when exposed to a simulated hygrothermal environment. Initially, glass fibre yarns, resins and single yarn composites were exposed to 60oC at 98% relative humidity for up to 3000 hours. This was followed by the thermal, chemical, and tensile property characterization of the constituent materials. The interfacial shear strength of the composites was then determined by performing single yarn fragmentation tests. Finally, the morphology of fibre yarns and the fibre/matrix interface was observed under the scanning electron microscope. Results showed that the resin types have a major role in the durability properties of glass fibre yarn composites. Bio-epoxy and vinyl ester resin exhibited thermal stability after exposure to a hygrothermal environment for 3000 hours, with the glass transition temperature of epoxy increased by 19oC. FTIR spectra reveal that bio-epoxy was chemically stable, while epoxy and vinyl ester resin have undergone chemical degradation because of hydrolysis. The tensile strength of fibre yarn was reduced by 37.0% because of blistering at the fibre surface, while a reduction of 22.6%, 10.7%, and 20.4% was observed for epoxy, bio-epoxy, and vinyl ester, respectively. Moreover, the interfacial shear strength (IFSS) was decreased by 15.6%, 6.1%, and 25.8% for epoxy, bio-epoxy, and vinyl ester composites, respectively. SEM micrographs revealed that matrix failure was dominant for epoxy and bio-epoxy, whereas fibre/matrix debonding was dominant for vinyl ester. The Analytical Hierarchy Process showed that the bio-epoxy is the best-performing resin under hygrothermal conditions when the priority was to retain mechanical properties. The vinyl ester resin is the best choice if the physical or thermal properties are considered the most important criterion.
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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".