The Influence of Clay Dispersion, Clay Concentration and Epoxy Chemistry on the Fracture Toughness of epoxy Nanocomposites
Bibliographic record
Abstract
The effect of epoxy chemistry, level of clay dispersion, and clay concentration on the fracture toughness of epoxy nanocomposites was studied. Three epoxy matrices with different glass transition temperatures (Tg) were chosen for this work. The epoxy resin EPON 828 cured with Jeffamine D-2000 is a rubbery material with a glass transition temperature Tg = -46.3°C, while the material prepared with Jeffamine D-230 is a glassy solid with a higher Tg of 86.8°C. A glassy solid with a yet higher glass transition temperature of Tg = 150.4°C was obtained upon curing with BF3·MEA via homopolymerization of epoxy and hydroxyl groups. The nanocomposites were prepared with the organoclay Cloisite 30B from Southern Clay Products. Fracture toughness properties of epoxies and epoxy nanocomposites were measured according to ASTM standards D5045-99. Fracture surfaces were analyzed by field emission gun scanning electron microscopy (FEGSEM). The quality of dispersion and intercalation/exfoliation were analyzed by X-ray diffraction (XRD). The results indicate that fracture toughness of the epoxy systems is enhanced with the addition of nanoclay, but the level of increase in fracture toughness depends very much on the clay dispersion, the clay concentration, and the nature of the epoxy.
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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) | 0.001 | 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".