Carboxyl-terminated butadiene-acrylonitrile-toughened epoxy/carboxyl-modified carbon nanotube nanocomposites: Thermal and mechanical properties
Bibliographic record
Abstract
Carboxyl-modified multi-walled carbon nanotubes (MWCNT-COOHs) as nanofillers were incorporated into diglycidyl ether of bisphenol A (DGEBA) toughened with carboxyl-terminated butadiene-acrylonitrile (CTBN). The carboxyl functional carbon nanotubes were characterized by Fourier-transform infrared spectroscopy and thermogravimetric analysis. Furthermore, cure kinetics, glass transition temperature (T g ), mechanical properties, thermal stability and morphology of DGEBA/CTBN/MWCNT-COOHs nanocomposites were investigated by differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), universal test machine, thermogravimetric analysis and scanning electron microscopy (SEM). DSC kinetic studies showed that the addition of MWCNT-COOHs accelerated the curing reaction of the rubber-toughened epoxy resin. DMA results revealed that T g of rubber-toughened epoxy nanocomposites lowered with MWCNT-COOH contents. The tensile strength, elongation at break, flexural strength and flexural modulus of DGEBA/ CTBN/MWCNT-COOHs nanocomposites were increased at lower MWCNT-COOH concentration. A homogenous dispersion of nanocomposites at lower MWCNT-COOH concentration was observed by SEM.
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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".