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Design and Experimental Demonstration of Impedance-Matched Circular-Polarization-Selective Surfaces with Spin-Selective Phase Modulations

2020· article· en· W3000012647 on OpenAlexaff
Minseok Kim, George V. Eleftheriades

Bibliographic record

VenuePhysical Review Applied · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsCircular polarizationImpedance matchingPolarization (electrochemistry)OpticsGeometric phaseElectrical impedanceCondensed matter physicsQuantum mechanicsMicrostrip

Abstract

fetched live from OpenAlex

We present the design and experimental demonstration of an impedance-matched circular-polarization-selective surface that also offers spin-selective phase modulations at microwave frequencies. We achieve this by leveraging the theory of Pancharatnam-Berry phase shift and cascading four tensor impedance layers, each comprising an array of crossed meander lines. These meander lines are precisely tuned and rotated to implement particular tensor surface impedance values to satisfy the impedance-matching condition for the transmitted right-handed circularly polarized field while inducing Pancharatnam-Berry phase shift for only the reflected left-handed circularly polarized field. We present a detailed numerical synthesis technique to obtain the required impedance values to satisfy the impedance-matching condition, and demonstrate spin-selective phase modulations based on Pancharatnam-Berry phase shifts. To verify the proposed idea, we experimentally demonstrate nearly reflectionless transmission of right-handed circular polarization at broadside and reflection of left-handed circular polarization at ${33}^{\ensuremath{\circ}}$ off broadside at 12 GHz. For this purpose, a free-space quasioptical setup and a near-field measurement system are used to measure the transmitted and reflected circularly polarized fields, respectively.

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 score0.077
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.320
Teacher spread0.285 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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