Relationship of Water Content With Silicon and Fluorine Contents of Silicone-Hydrogel Contact Lens Materials
Bibliographic record
Abstract
OBJECTIVES: The relationship between water (W) content and silicon (Si) content of silicone-hydrogel (SiHy) contact lens materials was inspected using identical methodologies, equipment, and operators for materials composing 16 types of commercially available SiHy contact lenses. Fluorine (F) content was included in the analysis for the three materials also containing a fluoropolymer. One type of lens consisted of a bulk SiHy material coated with thin layers of conventional hydrogel. METHODS: SiHy materials were obtained in the form of 16 contact lens brands purchased on the open market in a common range of refractive powers from -3 to +6 D in single lots. All test lenses were equilibrated at room temperature in a standard saline recommended in the American National Standards Institute Z80.20-2016 and International Organization for Standardization (ISO) 18369-4:2017 standards. W content was obtained gravimetrically, in %, according to those standards for 16 lenses of each SiHy material. Si content was determined in % using inductively coupled plasma optical emission spectroscopy for four digested lenses of each material. F content was determined in % using an ion-selective electrode for four combusted lenses of each of the three fluorinated SiHy materials. W and Si contents of the bulk SiHy material of the coated lens were estimated by computational exclusion of the hydrogel layers. RESULTS: The linear coefficients of determination (R, n=16) were -0.7576 (relating mean dry Si content [n=4] to mean W content [n=16]) and -0.8819 (relating mean hydrated Si content [n=4] to mean W content [n=16]). When the 4 SiHy materials that were fluorinated or coated were excluded from the analysis, the R values (n=12) were -0.8869 and -0.9263, respectively. When F contents and the coating were added to the assessments, the linear coefficients of determination (R, n=16) became -0.8948 (relating mean dry [Si+F] content to mean W content) and -0.9397 (relating mean hydrated [Si+F] content to mean W content). CONCLUSIONS: There is a fundamental negative linear relationship between Si and W contents for SiHy contact lens materials above 35% W content that is followed when F content and hydrogel coatings are empirically added to the analysis below 35% W content. The relationship was tightest for hydrated (Si+F) content and W content, for which the regression equation had an R of -0.9397: (Si+F)=-0.3073 (W)+22.148. The relationship between (Si+F) and W therefore seems to be based on composition rather than structure of available SiHy contact lens materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".