The Design Of Infrared Mirror Coatings For The Enhanced Performance Of Incandescent Lighting
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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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) | 0.000 | 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 | 0.001 | 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".