Comparative Study on Surface Modification of PVC and PTFE using Atmospheric Air Plasma and Low-Pressure Oxygen Plasma Treatments
Bibliographic record
Abstract
This study compares atmospheric air plasma and low-pressure oxygen plasma treatments on polyvinyl chloride (PVC) and polytetrafluoroethylene (PTFE) to improve surface properties without affecting the bulk.Atmospheric plasma was released at ambient pressure using air, and low-pressure plasma employed pure oxygen in a vacuum chamber for controlled activation.Surface characterization was performed using X-ray Photoelectron Spectroscopy (XPS), Atomic Force Microscopy (AFM), contact angle measurements, and Owens-Wendt analysis.XPS revealed the development of polar groups such as C=O and O-C=O, with broader oxidation detected under atmospheric plasma, and a dominating O-C=O peak following low-pressure treatment, especially in PVC, and more selective functionalization under low-pressure plasma, particularly for PTFE.Contact angle dropped from 81° to 23.7° in PVC and from 91° to 86° in PTFE with low-pressure plasma.Surface energy increased significantly, with PVC showing the highest polar component.AFM revealed that atmospheric plasma increased surface roughness due to etching, while low-pressure plasma maintained smoother surfaces, indicating chemical modification.Overall, atmospheric plasma combines chemical and physical effects, while lowpressure plasma enables controlled, cleaner functionalization, guiding treatment choices based on material and application needs.
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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".