Effects of composition of hardener on the curing and aging for an epoxy resin system
Bibliographic record
Abstract
Abstract Different mixture ratios of Shell Epon 828 (based on diglycidyl ether of bisphenol A, DGEBA) and Shell EPI‐CURE 3046 (based on triethylenetetramine, TETA) were evaluated under different environments of isothermal curing at 80°C in DSC, room temperature curing in air, and aging in water at 45°C. The curing reactions were monitored using differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and infrared spectroscopy (IR). It was shown that the initial curing rate increased with the amount of hardener. However, the epoxy groups in samples with excess hardener were prone to reaction with primary amines located at the ends of TETA molecules, resulting in a less dense epoxy network. During aging in water at 45°C, significant effects of water on the postcure and the increased water absorption with an increase of hardener amount were observed. The DMA results show that the samples with hardener around stoichiometric composition have the greatest storage modulus while curing in air environment. However, the samples with hardener much less than stoichiometric composition have greater storage modulus under aging in water at 45°C. in water at 45°C. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 580–588, 2006
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".