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Record W3014304319 · doi:10.1109/jstqe.2020.2984559

Ultra-Wide Spectral Bandwidth and Enhanced Absorption in a Metallic Compound Grating Covered by Graphene Monolayer

2020· article· en· W3014304319 on OpenAlexaff
Nghia Nguyen-Huu, Jaromı́r Pištora, Michael Čada, T. Nguyen‐Thoi, Youqiao Ma, Kiyotoshi Yasumoto, B. M. A. Rahman, Qiang Wu, Yuan Ma, Quang Hieu Ngo, Jie Lin, Hiroshi Maeda

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGrapheneMaterials scienceOptoelectronicsAbsorptanceGratingOpticsMonolayerNanotechnology

Abstract

fetched live from OpenAlex

Graphene, a two-dimensional monatomic layer of carbon material, has demonstrated as a good candidate for applications of ultrafast photodetectors, transistors, transparent electrodes, and biosensors. Recently, many studies have shown that using metallic deep gratings could enhance the absorptance of graphene of 2.3% up to 80% in the near infrared region for applications in photon detection. This paper presents utilizing a nanograting structure, namely, a compound metallic grating could greatly enhance the absorptance of graphene up to 98% and widen its spectral bandwidth to 0.6 μm, which are greater than those of previous work. The study also showed that the absorptance spectrum is insensitive to angles of incidence. Furthermore, the proposed graphene-covered compound grating might bring a lot of benefits for graphene designs-based optical and optoelectronic devices.

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 score0.425
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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