Quantification of Matrix and Reinforcement Effects on the Young’s Modulus of Carbon Nanotube/Epoxy Composites using a Design of Experiments Approach
Bibliographic record
Abstract
The focus of this work is to present a methodology to systematically study and classify the influence of a set of controllable parameters during the fabrication of carbon nanotube (CNT)/epoxy co mposites on the material's elastic modulus. The chosen factors include two types of poly mer matrices (i.e., LY 5052 and LY 564), two types of carbon nanotubes (i.e., single- and mult i-walled carbon nanotubes), functionalized and pristine carbon nanotubes, and different weight-percents (wt.%) of CNTs. A factorial design of experiment (DOE) with mixed levels has been employed to estimate the contribution of the aforementioned factors, along with their interactions, in the maximization of the Young's modulus of the fabricated CNT/epoxy co mposites. Over 120 specimens were fabricated and tensile tests were carried out to obtain an optimu m Young's modulus of the CNT/epo xy composite. The results indicate that among control parameters, the wt.% of CNTs and the type of CNTs have the highest effects, whereas their interaction has the least effect. It is also shown that the functionalized CNTs can significantly dimin ish the effect of noise factors, arising fro m the CNT wav iness, debonding between CNTs and polymer matrices, the random orientation of CNTs and non-uniform CNT d ispersion. Among tested material configurations, the highest Young's modulus (4.135 GPa) was achieved on the functionalized single-walled carbon nanotube/amine resin LY5052 containing 1.5 wt.% of CNTs. This corresponded to a 33% imp rovement co mpared to the pure epoxy resin LY5052. The presented methodology is straightforward and can be applied to other types of CNT-reinforced co mposites.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".