Plasma-based introduction of monosort functional groups of different type and density onto polymer surfaces. Part 2: Pulsed plasma polymerization
Bibliographic record
Abstract
This new functionalization method consists of the deposition of very thin plasma polymer layers (20 to 100 nm) of functional group bearing monomers in pulsed plasma. With allylalcohol, a maximum of 30 OH groups per 100 C atoms was measured with a selectivity of about 90% and a significant stability at long-time exposure to air. Allylamine was used to produce primary amino groups, with a maximum of 18 NH2groups per 100 C atoms. Side reactions were observed during the storage in air, such as oxidation of the amino groups. Carboxylic groups could be produced using acrylic acid with a maximum concentration of 24 COOH groups per 100 C atoms. The most prominent side reaction was the decarbonylation/ decarboxylation of the acid group during plasma deposition. The variation of the density of functional groups using the pulsed-plasma polymerization of functional-group-bearing monomers was possible by the chemically-initiated radical copolymerization with either a chain-extending monomer, such as ethylene, or a cross linker, such as butadiene, in plasma. The density of functional groups could be adjusted continuously (0 to 30 OH, 0 to 18 NH2 and 0 to 24 COOH groups per 100 C atoms). The successful application of these densely functionalized polymer surfaces for producing biocompatible surfaces and for use in metal–polymer composites is proposed.
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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".