Silver nanowire infused hydrogel contact lenses for non-invasive intraocular pressure monitoring
Bibliographic record
Abstract
Abstract Flexible, hydrogel-based materials are now vital in advanced diagnostic devices, particularly in smart contact lenses for continuous health monitoring. This study introduces a novel composite material by infusing silver nanowires (AgNWs) into a hydrogel polymer, creating a flexible, sensitive contact lens capable of continuous intraocular pressure (IOP) monitoring. The fabrication process is streamlined to integrate seamlessly with industrial production, promoting scalability and mass manufacture. The effect of AgNW integration on the hydrogel’s Young’s modulus was assessed through tensile testing of flat sheet samples at 5%, 3%, and 1% nanowire concentrations by weight, yielding modulus values of 2.517, 2.081, and 1.669 MPa, respectively, with stiffness increasing alongside AgNW concentration. The device functions by detecting frequency shifts caused by impedance changes from structural reconfiguration of the nanowire network under applied pressure. To evaluate performance, lenses of various concentrations were rigorously tested for stability, repeatability, and sensitivity using synthetic, porcine, and human cadaveric eye models. Results indicated that higher AgNW concentrations achieved greater sensitivity, with peak sensitivities of 12, 30, and 18 kHz mmHg−1 recorded at a 5% concentration across the testing models. This material represents a promising candidate for scalable, high-performance IOP monitoring applications.
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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".