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Record W3042686070 · doi:10.3390/nano10071385

Bragg-Mirror-Assisted High-Contrast Plasmonic Interferometers: Concept and Potential in Terahertz Sensing

2020· article· en· W3042686070 on OpenAlexaff
Youqiao Ma, Jinhua Li, Zhanghua Han, Hiroshi Maeda, Yuan Ma

Bibliographic record

VenueNanomaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTerahertz radiationAstronomical interferometerPlasmonOpticsHigh contrastContrast (vision)Materials scienceOptoelectronicsInterferometryPhysics

Abstract

fetched live from OpenAlex

A Bragg-mirror-assisted terahertz (THz) high-contrast and broadband plasmonic interferometer is proposed and theoretically investigated for potential sensing applications. The central microslit couples the incident THz wave into unidirectional surface plasmon polaritons (SPPs) waves travelling to the bilateral Bragg gratings, where they are totally reflected over a wide wavelength range back towards the microslit. The properties of interference between the SPPs waves and transmitted THz wave are highly dependent on the surrounding material, offering a flexible approach for the realization of refractive index (RI) detection. The systematic study reveals that the proposed interferometric sensor possesses wavelength sensitivity as high as 167 μm RIU−1 (RIU: RI unit). More importantly, based on the intensity interrogation method, an ultrahigh Figure-of-Merit (FoM) of 18,750% RIU−1, surpassing that of previous plasmonic sensors, is obtained due to the high-contrast of interference pattern. The results also demonstrated that the proposed sensors are also quite robust against the oblique illumination. It is foreseen the proposed configuration may open up new horizons in developing THz plasmonic sensing platforms and next-generation integrated THz circuits.

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 categoriesMeta-epidemiology (narrow)
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 score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.221
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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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