Designing High Reflectivity Omnidirectional Coating of Mirrors for Near Infrared Spectrum (700-2500 nm)
Bibliographic record
Abstract
In this paper, a high reflection coating is designed depending on the variable of refractive indices for NIR spectral region (700-2500 nm) by the use of the computer program MATLAB version 7. We could find the reflective 99.62% for seven layers at the incident angels (90°, 40°) in the wavelength (1064 nm) for coatings (Si, MgF2), substrata BK7 (relatively hard borosilicate crown glass with high homogeneity), which is used for laser application such as the ND:YAG laser (1060 nm), and R=98.37% for coatings (SbSe, Na3AIF6) and substrata glass for eleven layers at ?=90° which covers the wavelength from (955.6 nm) to (1622 nm) and represents the complete range for optical telecommunication band (short (S) 1460-1530 nm, conventional (C) 1530-1560 nm and long (l)1560-1620 nm). The results show that the reflectivity of the stack increases with the number of layers in the stack, the best layer number is nine which has a reflective of 99.62% at (1060 nm), as shown in Figure 4a. Also the reflective changes with incident angel; the best angel is (40°) which gives the convergent reflective for electric and magnetic polarization 99.91% and 99.36%, respectively for the wavelength (1060 nm).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".