Comparing the Effects of ZnO and ZrO2 Nanomaterials on the Mechanical, Chemical, and Crystalline Properties of Epoxy Resin (DGEBA)
Bibliographic record
Abstract
This research paper presents a comparative experimental study on the impact of zinc oxide and zirconium dioxide nanomaterials on the chemical, mechanical, and crystalline properties of epoxy resin (diglycidyl ether of bisphenol-A).Nanomaterials were incorporated into the epoxy resin at three different concentrations (4%, 6%, and 8%) by weight.Results indicated enhanced properties of the epoxy resin, including tensile and compressive strengths, as well as improvements in chemical and crystalline characteristics, assessed through scanning electron microscope (SEM) and Fouriertransform infrared spectroscopy (FTIR).Notably, zirconium dioxide exhibited superior performance across all properties, enhancing tensile and compressive strengths by 67% and 50%, respectively.Zinc oxide, at the same concentrations, led to a 50% increase in tensile strength and a 40% increase in compressive strength.These outcomes were observed at the highest concentration (8%wt) of both nanomaterials and the pure epoxy resin.The presence of nanomaterials at this ratio promoted greater cohesion within the composite, as evidenced by SEM images of selected samples.SEM analysis highlighted the pivotal role of ZrO2 nanoparticles in improving epoxy integration, surface quality, crystallization, and imperfection removal, crucial factors for enhancing composite materials.FTIR analysis of the resin containing ZrO2 nanoparticles revealed shifts and alterations in peaks, indicating successful nanoparticle-epoxy interaction, resulting in notable structural changes.
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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".