Influence of the reaction stoichiometry on the mechanical and thermal properties of SWCNT-modified epoxy composites
Bibliographic record
Abstract
Previous studies suggest that carbon nanotubes (CNTs) have a considerable influence on the curing behavior and crosslink density of epoxy resins. This invariably has an important effect on different thermal and mechanical properties of the epoxy network. This work focuses on the important role of the epoxy/hardener mixing ratio on the mechanical and thermal properties of a high temperature aerospace-grade epoxy (MY0510 Araldite as an epoxy and 4,4'-diaminodiphenylsulfone as an aromatic hardener) modified with single-walled carbon nanotubes (SWCNTs). The effects of three different stoichiometries (stoichiometric and off-stoichiometric) on various mechanical and thermal properties (fracture toughness, tensile properties, glass transition temperature) of the epoxy resin and its SWCNT-modified composites were obtained. The results were also supported by Raman spectroscopy and scanning electron microscopy (SEM). For the neat resin, it was found that an epoxy/hardener molar ratio of 1:0.8 provides the best overall properties. In contrast, the pattern in property changes with the reaction stoichiometry was considerably different for composites reinforced with unfunctionalized SWCNTs and reduced SWCNTs. A comparison among composites suggests that a 1:1 molar ratio considerably outperforms the other two ratios examined in this work (1:0.8 and 1:1.1). This composition at 0.2 wt% SWCNT loading provides the highest overall mechanical properties by improving fracture toughness, ultimate tensile strength and ultimate tensile strain of the epoxy resin by 40%, 34%, 54%, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".