Determination of Optical Constant of a Clear Glass Material using Spectrophotometer Measurements
Bibliographic record
Abstract
Many qualities make glass attractive, as it is transparent, chemically inert, environmentally friendly, sustainable, strong, easily available and relatively cheap.Recently, many researchers have been interested in designing buildings to get the benefit from daylight inside, which saves a lot of building consumption for artificial lighting, which gives visual and thermal comfort and also contributes to reducing costs.The complex refractive index of glass is a very crucial concept because it determines not only how much light is reflected and transmitted, but also its angle of refraction in glass.The optical constants of glass material are very useful for determining its radiative properties, as well as for selecting the appropriate thin-film coatings on a glass substrate.The objective of this study is to calculate the real part (n) and the imaginary part (k) of the complex refractive index of a clear glass material using a simple method based on the reflectivity and transmissivity measurements.In this study, the parts n and k are derived from the equations of the reflectivity at near zero incidence and transmissivity at normal incidence using Shimadzu IR-70 Spectrophotometer and Cary 5E Spectrophotometer apparatuses.The real and the imaginary parts of the complex refractive index of the glass sample obtained in the present study are in very good agreement with Rubin's data.However, a direct comparison between different samples is not possible, due to the difference in manufacturing process and material composition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".