Dispersion stability in carbon nanotube modified polymers and its effect on the fracture toughness
Bibliographic record
Abstract
In this paper, the dispersion stability of multiwall carbon nanotubes (MWNTs) mixed with an epoxy resin is studied. An instrumented optical microscope with a hot stage was used to study the evolution of the carbon nanotubes (CNTs) dispersion during the cure of the resin. A new image processing approach is then introduced to quantify dispersion and identify the source of dispersion degradation during the cure. The results showed that the reduction of the resin viscosity at temperatures greater than 100 °C caused an irreversible re-agglomeration of the CNTs in the matrix. It was shown that the fine-tuning of the ratio and type of curing agent as well as the curing temperature directly affect the dispersion stability of MWNTs in the epoxy polymer. The dispersion quality was then directly correlated to the fracture toughness of the modified resin and a maximum of 20% improvement was achieved.
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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 | 0.001 |
| 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".