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Record W2900573398 · doi:10.25071/10315/35376

The Design Of Infrared Mirror Coatings For The Enhanced Performance Of Incandescent Lighting

2018· article· en· W2900573398 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIncandescent light bulbInfraredMaterials scienceOptoelectronicsOpticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

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 %.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.224
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it