Fragmentation Mechanism in a Nitrogen Dielectric Barrier Discharge Plasma on Fluoropolymer Polymer Films
Bibliographic record
Abstract
Due to their chemical inertness and low friction coefficient, fluoropolymers are today widely employed in sectors of activity as diverse and distinct as the textile industry, architectural sector, and medicine. However, their low surface energy results in poor adhesion, for example, when used for a component in a composite device with multiple other materials. Among the techniques used to enhance their adhesion, atmospheric pressure discharges provide a fast and low-cost method with a reduced environmental impact. Although this approach has proven to be efficient, the different chemical and physical processes in the discharge remain not fully understood. In this study, fluoropolymer surfaces were modified using an atmospheric pressure dielectric barrier discharge in a nitrogen and organic precursor environment. To prevent any damage to fluoropolymer surfaces, the dissipated power in the discharges was tuned by applying a duty cycle. Evidence shows that plasma treatment allows for the incorporation of oxygen and nitrogen in the surface resulting in the formation of hydrophilic functionalities such as carbonyl groups both in ketone and amide form, amine, and hydroxyl groups after 180 s of treatment. Overall, the data reveal that the discharge duty cycle has more effect on the oxygen and carbon content in the coating than the precursor concentration. In addition, increasing the precursor concentration limits the molecular fragmentation and nitrogen incorporation into the coating. These experiments enable the building of a better fundamental understanding of the formation mechanism of such chemical moieties at the fluoropolymer surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".