The Adhesive Strength of Epoxy/Sol-Gel Materials Modified by Various Ratio of γ-Al2O3 Nanoparticles
Bibliographic record
Abstract
In this study, the use of sol-gel/epoxy adhesive based on the combination of organic and inorganic components within the adhesive matrix have been studied. The combination of different amounts of ᵞ-Al2O3 nano-particles to the adhesive matrix was evaluated. Mild steel specimens were prepared for lap joints, which were cured in an oven at 200°C for 16 hours. The bond strength of the sol-gel/epoxy matrix was investigated using a universal tensile test machine. The presence of Al-OH and/or Si-OH bonds increases causing an increase in the strength of the bulk material. This process is seen through the appearance of an absorption peak shoulder which appears in the range of ~ 1088 to1100 cm-1 which corresponds to Al-O-Si or Si-O-Si. The maximum adhesive strength of composite sol-gel/epoxy adhesive recorded was 23±0.4 MPa. This was obtained when small amounts of γ-Al2O3 nano-particles (4.0 wt%), were incorporated within the matrix. However, as the level of these inorganic materials in the adhesive matrix increased further, the adhesive shear strength gradually decreased. At a high ratio of γ-Al2O3 particles, poor interfacial bonding or adhesion between the filler and the adhesive matrix is recorded. Scanning Electron Microscopy (SEM) is used to investigate the fracture surface with 4.0 wt% γ-Al2O3, the scanning shows a very small distance of cracks, suggesting the material may act as a barrier to crack propagation and thus increases the energy required for fracture.
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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) | 0.001 | 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".