Influence of Zinc Oxide and Titanium Dioxide Nanoparticles on Kevlar/Epoxy Composites
Bibliographic record
Abstract
This research was aimed to perform a symmetric investigation regarding the influence of two types of nanoparticles, on some mechanical and morphological behaviors of the Kevlar/epoxy nanocomposites.Composites were prepared by dispersing nanoparticles of nano ZnO and TiO2 at loading of 1, 2 and 4 wt.% of each in (Kevlar/epoxy) composite using high speed mechanical mixer followed by sonication, then injecting them inside the cavity of a two plates glass mold.After curing they were characterized by tensile tests, flexural test, creep test and SEM.The results showed that the maximum increase of mean stress (41%) and the maximum flexural strength was obtained for system containing 4 wt.% of both nano ZnO and TiO2 particles, i.e., EKZ4 and EKT4 .SEM images for processed specimens were taken to observe the analogy distribution of both nanoparticles through the fiber/epoxy system.The improvement in the mechanical properties of nanocomposite EKT2 (2% TiO2)and EKZ2 (2% ZnO) attributed by the homogeneity of the nanoparticles distribution inside the epoxy which appear in SEM images.Also, the semispherical shape of nanoparticles led to uniform distribution of the nano particles inside Epoxy-keflar resin.
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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".