Curing kinetics and mechanical properties of epoxy nanocomposites based on different organoclays
Bibliographic record
Abstract
Abstract The effect of different organoclays and mixing methods on the cure kinetics and properties of epoxy nanocomposites based on Epon828 and Epicure3046 was studied. The two kinds of organoclay used in this study, both based on natural montmorillonite but differing in intercalant chemistry, were I.30E (Nanomer I.30E—treated with a long‐chain primary amine intercalant) and C.30B (Cloisite 30B—treated with a quaternary ammonium intercalant, less reactive with epoxy than the primary amine). The two mixing processes used to prepare the nanocomposites were (i) a room‐temperature process, in which the clay and epoxy are mixed at room temperature, and (ii) a high‐temperature process, in which the clay and epoxy are mixed at 120°C for 1 h by means of mechanical mixing. The nanocomposites were cured at room temperature and at high temperature. The quality of dispersion and intercalation/exfoliation were analyzed by scanning electron microscopy, transmission electron microscopy, and X‐ray diffraction. The heat evolution of the epoxy resin formulation and its nanocomposite systems was measured using differential scanning calorimetry at different heating rates of 2.5, 5, 10, 15, and 20°C min −1 . The cure kinetics of these systems was modeled by means of different approaches. Kissinger and isoconversional models were used to calculate the kinetics parameters while the Avrami model was utilized to compare the cure behavior of the epoxy systems. The cure kinetics and mechanical properties were found to be influenced by the presence of nanoclay, by the type of intercalant, and by the mixing method. POLYM. ENG. SCI., 47:649–661, 2007. © 2007 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".