Controlled swelling of biomaterial devices for improved antifouling polymer coatings
Bibliographic record
Abstract
Nonspecific interactions between cells and implantable elastomers often leads to failure modes for devices such as catheters, cosmetic and reconstructive implants, and sensors. To reduce these interactions, device surfaces can be coated with hydrophilic polymers, where greater polymer density enhances antifouling properties. Although graft-from coating techniques result in higher density polymer films and lower fouling in controlled settings, simpler graft-to methods show similar results on complex implanted devices, despite limited density. To address the need for improved graft-to methods, we developed Graft then shrink (GtS) where elastomeric materials are temporarily swollen during polymer grafting. Herein, we demonstrate a graft-to based method for poly(oligo(ethylene glycol) methyl ether methacrylate) (pOEGMA) on swollen silicone, GtS, that enhances grafted polymer content and fouling resistance. Total grafted polymer content of pOEGMA on toluene swollen silicone increased over ~ 13 × compared to non-swollen controls, dependent on the degree of silicone swelling. Increases in total grafted polymer within the top 200 µm of the material led to bacterial and mammalian cell adhesion reductions of 75% and 91% respectively, compared to Shrink then Graft (StG) antifouling polymer coated controls. GtS allows for the simple 3D coating of swellable elastomers (e.g., silicone medical devices) with improved antifouling pOEGMA coatings.
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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) | 0.001 | 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".