A Comprehensive Study on the Effect of ZnO and ZnAl2O4 Nanoparticles on the Mechanical Properties of Epoxy Coating: Tensile and Hardness
Bibliographic record
Abstract
ZnO and ZnAl2O4 nanoparticles (NPs) were successfully prepared by the co-precipitation method and characterized by x-ray powder diffraction, transmission electron microscopy, and Fourier transform infrared spectroscopy. The prepared NPs were incorporated into epoxy (EP) coating with mass ratios 200 800 mg/kg of ZnO NPs/EP and ZnAl2O4 NPs /EP. The prepared coatings were characterized by scanning electron microscopy and Fourier transform infrared spectroscopy, and their mechanical properties were investigated, at room temperature, after 5, 10, 15, and 20 days of preparation. Tensile tests showed an improvement in the tensile properties, with the best improvement in ultimate tensile strength (93.2%) for 800 mg/kg ZnAl2O4 NPs/EP coating after 15 days of preparation. The ZnO NPs/EP and ZnAl2O4 NPs/EP coatings exhibited noticeable sensitivity to the stretching rate. Vickers microhardness (Hv) investigations showed normal indentation size effect behavior for all the samples. The best improvement in Hv was attained after 5 days of preparation, for all coatings, with the best improvement (9.15%) for 700 mg/kg ZnO NPs/EP.
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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".