MétaCan
Menu
Back to cohort
Record W4403771238 · doi:10.1038/s41598-024-75430-0

Active infrared tuning of metal–insulator-metal resonances by VO2 thin film

2024· article· en· W4403771238 on OpenAlexaff
Emilija Petronijevic, Maria Cristina Larciprete, Marco Centini, Lucilla Pronti, Vincenzo Aglieri, Luca Razzari, Andréa Toma, Roberto Macaluso, Roberto Li Voti, C. Sibilia

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEuropean Commission
KeywordsMetalInfraredMaterials scienceMetal-insulator-metalThin filmOptoelectronicsInsulator (electricity)NanotechnologyPhysicsOpticsElectrical engineeringMetallurgyVoltageCapacitorEngineering

Abstract

fetched live from OpenAlex

VO 2 is a promising phase change material offering a large contrast of electric, thermal, and optical properties when transitioning from semiconductor to metallic phase. Here we show that a hybrid metamaterial obtained by proper combination of a VO 2 layer and a nanodisk gold array provides a tunable plasmonic gap resonance in the infrared range. Specifically, we have designed and fabricated a metal–insulator-metal gap resonance by inserting sub-wavelength VO 2 film between a flat gold layer and a gold nanodisk resonator array. The resonance of the hybrid metamaterial is centered in the useful 3–5 μm range when VO 2 is in its semiconductor state. The experimental study highlights a monotonical spectral tuning of the resonance when increasing temperature up to 50 °C above the room temperature, providing a continuous resonance shift of almost 1 μm in the mid-infrared range. Wavelength range and intensity tunability can be further optimized by modifying the thicknesses of the layers and metamaterial parameters.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicTransition Metal Oxide NanomaterialsFrench-language works237,207