Effect of blend composition on the adhesion strength of <scp>EVA‐g‐MA</scp>/<scp>LLDPE‐g‐MA</scp> films to epoxy substrates
Bibliographic record
Abstract
Abstract Heat shrink sleeves (HSS) are multilayer polymers that provide corrosion resistance for welding joints of steel pipes. Within the HSS structure, the adhesive layer is a blend of components, such as ethylene vinyl acetate (EVA) and linear low‐density polyethylene (LLDPE). In this study, a systematic approach was employed to investigate the effect of the rheology and blend composition of the maleic anhydride‐modified EVA and maleic anhydride‐modified LLDPE (EVA‐g‐MA/LLDPE‐g‐MA) adhesive layer, prepared by melt processing, on its adhesion properties. Various characterization techniques were employed to investigate the local morphology of the prepared blends at different zones. Peel strength tests were used to characterize the adhesion strength of the prepared adhesives. Our findings demonstrate that the local distribution of EVA‐g‐MA within LLDPE‐g‐MA plays a crucial role in the adhesion strength of the adhesive. The results show that the adhesion strength enhanced by nearly 45% as the composition of EVA‐g‐MA at the surface went up by 20%, shifting the failure location from the epoxy/adhesive interface to the bulk of the adhesive layer. Our hypothesis of the decrease of EVA‐g‐MA intrachain bonding and increase in the EVA‐g‐MA free chain amount in the presence of LLDPE‐g‐MA to improve the adhesion properties was corroborated through our calorimetry results where the relative EVA‐g‐MA crystallinity decreased as LLDPE‐g‐MA was introduced to the blend. The findings of our study highlight the importance of rheological behavior and blend composition in obtaining optimized performance of adhesives within the HSS structure. Highlights Compression molding of EVA‐g‐MA/LLDPE‐g‐MA induces a layered morphology. Local distribution EVA‐g‐MA/LLDPE‐g‐MA components influence their adhesion. EVA‐g‐MA at the interface improves the adhesion of EVA‐g‐MA/LLDPE‐g‐MA. LLDPE‐g‐MA suppresses the EVA‐g‐MA crystallinity. EVA‐g‐MA/LLDPE‐g‐MA composition affects adhesion at different temperatures.
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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 | 0.001 |
| 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".