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Record W2999203611 · doi:10.1063/1.5125652

Conductor-backed dielectric metasurface thermal emitters for mid-infrared spectroscopy

2020· article· en· W2999203611 on OpenAlexafffund
Muhammad Osama Ali, R. Niall Tait, Shulabh Gupta

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceEmissivityOptoelectronicsDielectricPermittivityInfraredCommon emitterGround planeResonatorSiliconOpticsAntenna (radio)Physics

Abstract

fetched live from OpenAlex

A conductor-backed dielectric metasurface thermal emitter at mid-IR frequencies with narrowband emissivity is experimentally demonstrated. The metasurface emitter consists of a high permittivity silicon resonator on top of a ground plane, whose resonant mechanism is explained using image theory. The resonator, placed close to a copper ground plane, is designed to produce a magnetic resonance, resulting in a low-profile device with a single emission peak in its subwavelength frequency range. The thermal emitter is next fabricated using common CMOS processes. Frequency dependent optical constants of plasma-enhanced chemical vapor deposited films of Si, SiO2, and evaporated Cu are also reported in the mid-IR range. Narrowband thermal emission is successfully obtained at around 7.22μm (41.5 THz), which corresponds to the absorption band of SO2. The Q-factor of about 37 is achieved with a peak emissivity of 0.65, which is significantly higher compared to the reported Q-factors of state-of-the-art plasmonic resonators.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.485

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 head0.020
GPT teacher head0.224
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes2
Has abstractyes

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