Reinforcing effect of organoclay in rubbery and glassy epoxy resins, part 1: Dispersion and properties
Bibliographic record
Abstract
Abstract The reinforcing effect of organoclay in two epoxy matrices, one rubbery and one glassy, was studied. The rubbery and glassy epoxy matrices were chosen to have a very similar chemistry to minimize its impact on the comparison of properties. The epoxy resin was EPON™ 828, and the two hardeners were amine‐terminated polyoxypropylene diols, having different average molecular weights (MW) of 2000 and 230 g/mol, namely Jeffamine® D‐2000 and Jeffamine® D‐230, respectively. The nanocomposites were prepared with the organoclay Cloisite® 30B from Southern Clay Products. The quality of dispersion and intercalation/exfoliation was analyzed by means of X‐ray diffraction (XRD), field emission gun scanning electron microscopy (FEGSEM), and transmission electron microscopy (TEM). Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to study the curing reactivity and the thermal stability of the epoxy resin systems, respectively. Tensile properties and hardness of epoxy resin and epoxy nanocomposites were measured according to ASTM standards D638‐02 and D2240‐00, respectively. Fracture surfaces were also analyzed by FEGSEM. These two epoxy systems as well as their nanocomposites display totally different physical and mechanical behavior. It is found that the quality of clay dispersion and intercalation/exfoliation, and the mechanical behavior of the glassy and rubbery epoxy nanocomposites are distinct. The results also indicate that the presence of the clay does not significantly affect the Tg of either the rubbery or the glassy epoxy; however, the fracture surface and mechanical properties were found to be influenced by the presence of nanoclay. Finally, several different reinforcing mechanisms are proposed and discussed for the rubbery and glassy epoxy nanocomposites. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2008
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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".