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Record W2885125997 · doi:10.1088/1361-6463/aad70a

On the enhancement of light absorption in vanadium dioxide/1D photonic crystal composite nanostructures

2018· article· en· W2885125997 on OpenAlexaff
Arezou Rashidi, Ali Hatef, Abdolrahman Namdar

Bibliographic record

VenueJournal of Physics D Applied Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsNipissing University
Fundersnot available
KeywordsMaterials scienceComposite numberPhotonic crystalVanadium dioxideAbsorption (acoustics)OptoelectronicsNanostructureVanadiumNanotechnologyPhotochemistryChemistryComposite materialMetallurgyThin film

Abstract

fetched live from OpenAlex

Abstract The absorption behaviors of a 35 nm-vanadium dioxide (VO 2 ) layer grown on top of a 1D photonic crystal (Sio 2 /Si) 10 are investigated theoretically in the near infrared spectral range. VO 2 has an insulator-to-metal phase transition around a critical temperature of 68 °C. The filling fraction (f) is defined as the portion of the VO 2 in the metallic phase—i.e. f = 0 and f = 1 for semiconductor and metal phase, respectively, and 0 < f < 1 during intermediate stages of the transition. Through the transfer matrix method, the influence of the filling fraction on the absorption of the structure has been inspected. It is shown that near-unity absorption can be achieved through VO 2 semiconductor-to-metal phase transition. Moreover, the tunability of absorption at oblique incidence reveals higher values of absorption peaks in the metallic phase of VO 2 than the semiconducting one at each angle of incidence. In addition, there is sensitivity to the state of polarization, so that the absorption peaks for TE mode are higher than the TM case. Finally, adjusting the thickness of the VO 2 layer indicates the possibility of achieving nearly complete absorption in both the semiconductor and metal phases. We believe that these properties make our structure a suitable basis for designing VO 2 -based absorbers.

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 score0.061
Threshold uncertainty score0.696

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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