The Design Of Infrared Mirror Coatings For The Enhanced Performance Of Incandescent Lighting
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Bibliographic record
Abstract
Herein we present the design of infrared mirror coatings for the enhanced performance of incandescent lighting. We consider single and stacked dielectric mirrors comprised of alternating layers of TCO and SiO2 nanoparticle films to function as infrared mirrors that reduce heat losses in incandescent lights. In this work, thin-film theory was employed to develop MATLAB code that calculates the reflectance and transmittance spectra of dielectric mirrors. In order to validate the MATLAB code, we compared our results to experimental results reported in the literature as well as results calculated using COMSOL Multiphysics software. Our results show that an infrared dielectric mirror coated onto the glass bulb of an incandescent light can increase its efficiency by ~32 %. However, stacked dielectric mirror coatings prevent a significant portion of visible light from transmitting through the glass bulb, and consequently decrease the efficiency of incandescent lights by ~46 %.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 it