Moisture effects on FM300 structural film adhesive: Stress relaxation, fracture toughness, and dynamic mechanical analysis
Bibliographic record
Abstract
Abstract The behavior of cured FM300 epoxy, a structural film adhesive, subjected to partial and full moisture saturation has been evaluated. Three separate but interrelated test methods were used: stress relaxation, fracture toughness, and dynamic mechanical testing. The mechanical response of the epoxy due to increasing moisture content was dependent on the testing method. In stress relaxation testing, the epoxy was plasticized when partially saturated with moisture, but it became more rigid when fully saturated. The plasticization‐to‐stiffening transition was not observed in the other two test methods. Fracture testing showed that the material toughness increased with increasing moisture concentration: plasticization effects were dominant. Similar changes in the loss modulus were found in dynamic mechanical analysis. We propose that the differences in behavior have been due to differences in load levels and loading rates used in these probing techniques. Stress relaxation testing, at a relatively lower load and loading rate, appeared to be more sensitive to the localized interactions between the absorbed water molecules and the crosslinked structure. Higher loads and loading rates tended to reveal the bulk effects of plasticization only. Nevertheless, there was also strong evidence from glass‐transition temperature measurements that these moisture effects were mostly reversible. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 95:1285–1294, 2005
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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 | 0.001 |
| 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) | 0.003 | 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".