Polymer Co‐Coating of Gold Nanoparticles Enables Their Integration Into Contact Lenses for Stable, Selective Ocular Light Filters
Bibliographic record
Abstract
Abstract Chronic exposure to long‐wavelength (red) light is associated with increased incidence and progression of myopia in preclinical models. Contact lenses are worn worldwide while chronic exposure to red light occurs, presenting a natural platform to mitigate light‐induced myopia progression. However, the integration of stable red‐light filters into contact lenses remains elusive. Here, bipyramidal gold nanoparticles (BPs) are synthesized as selective, tunable red‐light blockers and a surface‐modification method is developed to homogeneously embed them within contact lenses during manufacturing. By functionalizing the BPs with a combination of poly(vinylpyrrolidone) and methacrylate monomer, stable dispersions within etafilcon A contact lenses before, during, and after lens manufacturing are achieved. This approach enables the optical properties of hydrogel‐integrated nanoparticles to resemble their colloidal counterparts. Moreover, these BP‐laden contact lenses are stable against conditions relevant to manufacturing, sterilization, and use, including autoclaving, UV irradiation, and long‐term storage. Taken together, this work demonstrates selective light filters and a facile methodology to homogeneously embed functional nanoparticles into hydrogel biomaterials, such as contact lenses, potentiating a platform to mitigate myopia‐associated red‐light exposure.
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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".