MétaCan
Menu
Back to cohort

Wavelength-selective emitters with pyramid nanogratings enhanced by multiple resonance modes

2016· article· en· W2290667122 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

VenueNanotechnology · 2016
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal emittanceMaterials scienceGratingOpticsWavelengthPoynting vectorPlasmonThermophotovoltaicOptoelectronicsTungstenResonance (particle physics)Magnetic fieldPhysicsBeam (structure)

Abstract

fetched live from OpenAlex

Binary gratings with high or low metal filling ratios in a grating region have been demonstrated as successful candidates in enhancing the emittance of emitters for thermophotovoltaics since they could support surface plasmons (SPs), the Rayleigh-Wood anomaly (RWA), or cavity resonance (CR) within their geometries. This work shows that combining a tungsten binary grating with a low and high filling ratio to form a pyramid grating can significantly increase the emittance, which is nearly perfect in the wavelength region from 0.6 to 1.72 μm, while being 0.1 at wavelengths longer than 2.5 μm. Moreover, the emittance spectrum of the hybrid tungsten grating is insensitive to the angle of incidence. The enhancement demonstrated by magnetic field and Poynting vector patterns is due to the interplay between SPs and RWA modes at short wavelengths, and CR at long wavelengths. Furthermore, a combined grating made of nickel is also proposed providing enhanced emittance in a wide angle of incidence.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.609

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 head0.004
GPT teacher head0.174
Teacher spread0.170 · 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