The Effect of Aluminium Surface Treatments on the Bonding Properties of Silica-Modified Epoxy Adhesive Joints: A Statistical Approach
Bibliographic record
Abstract
A full factorial design is carried out to investigate the effects of different surface treatments, the inclusion of silica microparticles and the use of wash primer on the apparent shear strength and adherent strength of single-lap aluminium joints. Scanning electron microscopy, surface energy and roughness measurements are performed to characterise the aluminium surface. The results show that the use of wash primer decreases the apparent shear strength of the joints significantly. The cohesive failure of the primer is the main cause of the reduction in strength. On the other hand, the inclusion of 10 wt.% of silica microparticles in the adhesive layers increases the shear strength by 26%. Surfaces treated with NaOH for one minute, without using a wash primer, result in the most resistant joint. In contrast to the apparent shear strength, adherent strength is most effective when only degreasing is considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".