Antifouling silicone hydrogel contact lenses via densely grafted phosphorylcholine polymers
Bibliographic record
Abstract
Silicone hydrogel contact lenses (CLs) permit increased oxygen permeability through their incorporation of siloxane functional groups. However, contact lens biofouling can be problematic with these materials; surface modification to increase lens compatibility is necessary for acceptable properties. This work focuses on the creation of an antifouling CL surface through a novel grafting method. A polymer incorporating 2-methacryloyloxyethyl phosphorylcholine (MPC), well known for its antifouling and biomimetic properties, was grafted to the model lens surfaces using surface-initiated atom transfer radical polymerization (SI-ATRP). The SI-ATRP modification generated a unique double-grafted polymeric architecture designed to resist protein adsorption through the presence of a surrounding hydration layer due to the PC groups and steric repulsion due to the density of the grafted chains. The polymer was grafted from model silicone hydrogel CL using a four-step SI-ATRP process. Attenuated total reflectance-Fourier transform infrared spectroscopy and XPS were used to confirm the surface chemical composition at each step of the synthesis. Both the surface wettability and equilibrium water content of the materials increased significantly upon polyMPC modification. The surface water contact angle was as low as 16.04 ± 2.37° for polyMPC-50 surfaces; complete wetting (∼0°) was observed for polyMPC-100 surfaces. A decrease in the protein adsorption by as much as 83% (p < 0.000 36) for lysozyme and 73% (p < 0.0076) for bovine serum albumin was observed, with no significant difference between different polyMPC chain lengths. The data demonstrate the potential of this novel modification process for the creation of extremely wettable and superior antifouling surfaces, useful for silicone hydrogel CL surfaces.
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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".