Effects of Chemical Composition and the Addition of H<sub>2</sub> in a N<sub>2</sub> Atmospheric Pressure Dielectric Barrier Discharge on Polymer Surface Functionalization
Bibliographic record
Abstract
We examined the effect of hydrogen content in various polymers in a N2/H2 discharge for surface amine functionalization. Three polymers (polyethylene (PE), polyvinylidene fluoride (PVDF), and poly(tetrafluoroethylene) (PTFE)) containing various amounts of hydrogen and fluorine were treated with an atmospheric pressure dielectric barrier discharge (DBD). While surface modification was observed on the PE and the PVDF in a pure N2 discharge, adding H2 in a N2 discharge was necessary to observe the fluorine etching on the surface of the PVDF and PTFE polymers. The presence of a slight amount of hydrogen in the gas mixture was also a prerequisite to the formation of amino groups on the surface of all three polymers (max NH2/C approximately 5%). Aging revealed that the modified polymer surfaces treated in a N2-H2 discharge were less prone to hydrophobic recovery than were surfaces treated in pure N2, due primarily to the presence of a higher density of polar groups on the surfaces. We demonstrated that H atoms in the discharge are necessary for the surface amine functionalization of polymers in a N2 atmospheric pressure DBD, regardless of polymer chemical composition. It is therefore possible to control the plasma functionalization process and to optimize the concentration and specificity of NH2 grafted onto polymer surfaces by varying the H2 concentration in a N2 atmospheric pressure DBD.
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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) | 0.000 | 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".