Calculation of Spectral Optical Constants Using Combined Ellipsometric and Reflectance Methods for Smooth and Rough Bulk Samples
Bibliographic record
Abstract
Spectra of the optical constants n and k of a substance are often deduced from spectroscopic measurements, performed on a thick and homogeneous sample, and from a model used to simulate these measurements. Spectra obtained for n and k using the ellipsometric method generally produce polarized reflectance simulations in strong agreement with the experimental measurements, but they sometimes introduce significant discrepancies over limited spectral ranges, whereas spectra of n and k obtained with the single-angle reflectance method require a perfectly smooth sample surface to be viable. This paper presents an alternative method to calculate n and k. The method exploits both ellipsometric measurements and s-polarized specular reflectance measurements, and compensates for potential surface scattering effects with the introduction of a specularity factor. It is applicable to bulk samples having either a smooth or a rough surface. It provides spectral optical constants that are consistent with s-polarized reflectance measurements. Demonstrations are performed in the infrared region using a glass slide (smooth surface) and a pellet of compressed ammonium sulfate powder (rough surface).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".