Plasma-Mediated Grafting of Poly(ethylene glycol) on Polyamide and Polyester Surfaces and Evaluation of Antifouling Ability of Modified Substrates
Bibliographic record
Abstract
A simple cold plasma technique was developed to functionalize the surfaces of polyamide (PA) and polyester (PET) for the grafting of polyethylene glycol (PEG) with the aim of reducing biofilm formation. The surfaces of PA and PET were treated with silicon tetrachloride (SiCl4) plasma, and PEG was grafted onto plasma-functionalized substrates (PA-PEG, PET-PEG). Different molecular weights of PEG and grafting times were tested to obtain optimal surface coverage by PEG as monitored by electron spectroscopy for chemical analysis (ESCA). The presence of a predominant C-O peak on the PEG-modified substrates indicated that the grafting was successful. Data from hydroxyl group derivatization and water contact angle measurement also indicated the presence of PEG after grafting. The PEG-grafted PA and PET under optimal conditions had similar chemical composition and hydrophilicity; however, different morphology changes were observed after grafting. Both PA-PEG and PET-PEG surfaces developed under optimal plasma conditions showed about 96% reduction in biofilm formation by Listeria monocytogenes compared with that of the corresponding unmodified substrates. This plasma functionalization method provided an efficient way to graft PEG onto PA and PET surfaces. Because of the high reactivity of Si-Cl species, this method could potentially be applied to other polymeric materials.
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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) | 0.000 | 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".