Improvement of PET surface hydrophilicity and roughness through blending
Bibliographic record
Abstract
Controlling the adhesion of the polymer surface is a key issue in surface science, since polymers have been a commonly used material for many years. The surface modification in this study includes two different aspects. One is to enhance the hydrophilicity and the other is to create the roughness on the PET film surface. In this study we developed a novel and simple approach to modify polyethylene terephthalate (PET) film surface through polymer blending in twin-screw extruder. One example described in the study uses polyethylene glycol (PEG) in polyethylene terephthalate (PET) host to modify a PET film surface. Low content of polystyrene (PS) as a third component was used in the system to increase the rate of migration of PEG to the surface of the film. Surface enrichment of PEG was observed at the polymer/air interface of the polymer film containing PET-PEG-PS whereas for the PET-PEG binary blend more PEG was distributed within the bulk of the sample. Furthermore, a novel method to create roughness at the PET film surface was proposed. In order to roughen the surface of PET film, a small amount of PKHH phenoxy resin to change PS/PET interfacial tension was used. The compatibility effect of PKHH causes the formation of smaller PS droplets, which were able to migrate more easily through PET matrix. Consequently, resulting in a locally elevated concentration of PS near the surface of the film. The local concentration of PS eventually reached a level where a co-continuous morphology occurred, resulting in theinstabilities on the surface of the film.
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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) | 0.000 | 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) | 0.001 | 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".