Influence of adding nanomaterials on shear properties of epoxy resin at different temperatures
Bibliographic record
Abstract
Adhesive joints play a vital role in different industries owing to their advantages and ease of application compared to other joining methods. This research focuses on enhancing the mechanical properties of epoxy adhesives by incorporating graphene nanoplatelets (G) and iron-oxide nanofillers (Fe3O4). Single-lap adhesive joints, including both G and Fe3O4 nanoparticles, are fabricated at 2%, 3%, and 4% weight percentages and tested under tensile load at ambient, 45°C, and 88°C. The results reveal that adding G and Fe3O4 nanofillers enhances shear strength at elevated and room temperatures without altering the epoxy glass transition temperature (Tg). Furthermore, G nanofiller performs better in improving shear strength than Fe3O4. The optimal weight percentage is identified as 3 wt% for G and Fe3O4, as higher percentages lead to decreased shear strength due to agglomerations. This study provides insight into tailoring epoxy adhesives for improved mechanical performance under varying temperature conditions.
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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".