Compositional design and Taguchi optimization of hardness properties in silicone-based ocular lenses
Bibliographic record
Abstract
A multi-component acrylate-based copolymer system especially designed for application as ocular lenses is developed through free-radical, bulk polymerization of a system containing hydroxyethyl methacrylate, methyl methacrylate, triethylene glycol dimethacrylate, dimethyl itaconate, 3-(trimethoxysilyl) propylmethacrylate, Polyhedraloligomeric silsesquioxane-acrylate (POSS-acrylate) and AIBN as an initiator. The progress of the reaction was monitored by Fourier transform infrared spectroscopy (FTIR). The effect of increasing concentration of the components on the hardness of the synthesized lenses was measured by Shore Durometer before and after immersion in PBS solutions. Extraction test method was performed to analyze the biocompatibility of the fabricated lenses. In this research the Taguchi method was employed to achieve the optimal hardness property which plays a critical role in final application of the lens materials. The Taguchi trial for ocular lens hardness was configured in an L16 orthogonal array, by five control factors, each with four level settings. The results showed that 3-(trimethoxysilyl) propyl methacrylate decreases and 2-hydroxyethylmethacrylate increases, polyhedraloligomeric silsesquioxane with a cage-like structure, methyl methacrylate and dimethyl itaconate increase the hardness. Proliferation and growth of the cells showed that there is no toxic substance extracted from the lenses which can interfere with the cell growth.
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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) | 0.000 | 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".