Polymerization compounding: Epoxy‐montmorillonite nanocomposites
Bibliographic record
Abstract
Abstract A strategy to design intercalated montmorillonite nanocomposites has been explored. A commercial organoclay, 1.34 TCN (Nanocor Inc.), with bis(2‐hydroxylethy1) methy1 tallow ammonium, was modified by tolylene 2,4‐diisocyanate (TDI) and bisphenol A (BA). Thermogravimetric analysis (TGA), Fourier transform infrared (FTIR) spectroscopy and X‐ray diffraction (XRD) results of unmodified and modified 1.34 TCN (1.34‐TDI‐BA) indicate that TDI and BA have reacted with hydroxy1 groups on the surface of 1.34 TCN and hydroxy1 groups in the interlayer of 1.34 TCN. Using a classical two‐stage cure process with diamine as curing agent, intercalated epoxy nanocomposites were prepared for both types of organoclays. XRD and TEM results showed that the basal spacing of clay in nanocomposites was 3.68 and 4.42 nm for 1.34 TCN and 1.34‐TDI‐BA, respectively. Dynamic mechanical analysis (DMA) was performed on both modified and unmodified organoclay composites. Modified organoclay composites were found to have enhanced storage moduli, particularly at temperatures higher than the glass transition, Tg, of the matrix. Glass transition temperatures extracted from linear viscoelastic data are found to be slightly higher for modified organoclay nanocomposites, indicating enhanced interactions between the modified organoclay and the epoxy matrix. These results were also confirmed by independent measurements of Tg using differential scanning calorimetry (DSC).
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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".