Epoxy nanocomposites: Analysis and kinetics of cure
Bibliographic record
Abstract
Abstract The effect of organo‐nanoclay (Nanomer I30E) on the cure mechanism and kinetics of epoxy nanocomposites based on Epon 828 and Epicure 3046 was studied by means of dynamic differential scanning calorimetry (DSC) at four heating rates (2.5, 5, 10, and 20°C·min−1) and by Fourier transform infrared (FT‐IR) spectroscopy. The DSC cure data for epoxy‐amine mixtures with and without nanoclay was modeled by means of different approaches; the Kissinger and isoconversional models were used to calculate the kinetics parameters while the Avrami model was utilized to compare the cure behavior of the two systems. The Nanomer I30E was shown to initiate rapid homopolymerization of the Epon 828 resin at temperatures above 180°C. For the epoxy‐amine mixtures, the presence of nanoclay had little effect on the cure kinetics in the early stages (i.e., at lower temperatures), and the apparent activation energy was around 60 kJ·mol−1. However, in the later stages, the apparent activation energy increased significantly in the absence of nanoclay, but did not do so when it was present. The presence of nanoclay also lowered the final glass transition temperature by about 4°C. Polym. Eng. Sci. 44:1132–1141, 2004. © 2004 Society of Plastics Engineers.
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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 | 0.001 |
| 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".