Influence of adding nanomaterials on shear properties of epoxy resin at different temperatures
Why this work is in the frame
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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 (Fe<sub>3</sub>O<sub>4</sub>). Single-lap adhesive joints, including both G and Fe<sub>3</sub>O<sub>4</sub> 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 Fe<sub>3</sub>O<sub>4</sub> 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 Fe<sub>3</sub>O<sub>4</sub>, 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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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 | 0.000 | 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) | 0.000 | 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 it