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Record W4205765464 · doi:10.1364/ol.449863

Broadband tunable metasurface platform enabled by dynamic phase compensation

2022· article· en· W4205765464 on OpenAlexaff
Chunsheng Guan, Rui Feng, Badreddine Ratni, Xumin Ding, Jianjia Yi, Ming Jin, Qun Wu, Shah Nawaz Burokur

Bibliographic record

VenueOptics Letters · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsAdvanced Micro Devices (Canada)
FundersNatural Science Foundation of Heilongjiang ProvinceNational Natural Science Foundation of China
KeywordsOpticsBroadbandVaricapCompensation (psychology)Wireless power transferPhase (matter)Antenna (radio)HolographyComputer scienceOptoelectronicsMaterials scienceWirelessPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Broadband metasurfaces have attracted significant attention for a variety of applications in imaging and communication systems. Here, a method to alleviate the chromatic aberrations issue is proposed in the microwave region using dynamic phase compensation enabled by a reconfigurable metasurface. The dispersion characteristic of the meta-atom implemented with varactor diodes can be flexibly manipulated electronically, such that the dispersion-induced phase distortions over a wide frequency band can be compensated dynamically to achieve broadband performances. Various aberration-free functionalities can be realized with the proposed active metasurface. Near-field measurements are performed on a fabricated prototype to demonstrate aberration-free beam bending and hologram imaging, showing good agreement with simulation results. Such an active metasurface platform paves the way to efficient devices for wireless power transfer, sensors, and communication and antenna systems at radio or much higher frequencies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2022
Admission routes1
Has abstractyes

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