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Record W4408564014 · doi:10.1109/tap.2025.3550713

Low-Profile Aperiodic Metasurfaces for High-Efficiency Achromatic Anomalous Reflection Over a Wide Bandwidth

2025· article· en· W4408564014 on OpenAlexaff
Vasileios G. Ataloglou, George V. Eleftheriades

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAperiodic graphAchromatic lensBandwidth (computing)Reflection (computer programming)OpticsDirectional antennaPhysicsComputer scienceAntenna (radio)TelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Metasurface (MTS) anomalous reflectors can enhance future wireless communication systems due to their ability to redirect electromagnetic waves and establish MTS-assisted communication links. However, MTS reflectors typically suffer from chromatic aberrations with the reflected angle shifting as the frequency varies. Herein, we design achromatic MTSs for anomalous reflection by using an integral-equation framework that fully accounts for the frequency dispersion of the individual scatterers (unit cells). Rather than engineering the dispersion of each MTS cell locally, we harness the emerging near-field interactions (evanescent waves) between the scatterers to realize anomalous reflection at a fixed angle over a wide frequency range. Three anomalous reflectors are designed and experimentally verified at 10 GHz, demonstrating high-performance metrics compared to traditional achromatic reflectors while employing simple, low-profile unit cells. The presented results pave an effective way to realize achromatic wave functionalities, such as anomalous reflection, refraction, or lensing, over a wide bandwidth with low-profile and highly efficient MTSs.

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)0.001
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 score0.408
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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