Enhancing adhesion of fluorinated ethylene propylene through atmospheric pressure nitrogen plasma treatment: a comprehensive adhesive selection approach for optimal peel strength characterization
Bibliographic record
Abstract
Fluoropolymer films were treated by atmospheric pressure nitrogen plasma in a roll-to-roll configuration. This treatment aimed to modify the surface properties of the fluoropolymer and assess its adhesion with commercial silicone, rubber, and acrylic adhesive tapes. Fluoropolymer films and adhesives were characterized using contact angle measurements, Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR), X-ray Photoelectron Spectroscopy (XPS), and T-peel tests. The plasma treatment resulted in increased wettability of the films, defluorination of the fluoropolymer surface, the introduction of oxygen- and nitrogen-containing functional groups on the surface, and a reduction in surface roughness. Peel strength increased at different levels, depending on the treatment speed and adhesive employed. Silicone adhesives did not present a significant increase on plasma-treated fluoropolymer films; however, they presented the higher peel strength against the untreated substrate. Acrylic adhesives are sensitive to fluoropolymer surface chemistry and can be used to evaluate different plasma treatments, while the highest adhesion was obtained with rubber adhesives. The results presented herein provide information about the appropriate selection of the type of adhesive for a specific application, as well as the evaluation of surface modifications by T-peel test.
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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) | 0.000 | 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 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".