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Record W4287027419 · doi:10.48550/arxiv.2108.03511

Lattice-plasmon induced asymmetric transmission in two-dimensional chiral arrays

2021· preprint· en· W4287027419 on OpenAlexaff
N. Apurv Chaitanya, Muhammad Abdullah Butt, Orad Reshef, Robert W. Boyd, Peter Banzer, Israel De Leon

Bibliographic record

VenueFigshare · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsMax Planck - University of Ottawa Centre for Extreme and Quantum PhotonicsUniversity of Ottawa
Fundersnot available
KeywordsPlasmonExcitationScatteringLattice (music)Condensed matter physicsDiffractionLattice planePhysicsPlasmonic nanoparticlesOpticsMaterials scienceQuantum mechanicsReciprocal lattice

Abstract

fetched live from OpenAlex

Asymmetric transmission - direction-selective control of electromagnetic transmission between two ports - is an important phenomenon typically exhibited by two-dimensional chiral systems. Here, we study this phenomenon in chiral plasmonic metasurfaces supporting lattice plasmons modes. We show, both numerically and experimentally, that asymmetric transmission can be achieved through an unbalanced excitation of such lattice modes by circularly polarized light of opposite handedness. The excitation efficiencies of the lattice modes, and hence the strength of the asymmetric transmission, can be controlled by engineering the in-plane scattering of the individual plasmonic nanoparticles such that the maximum scattering imbalance occurs along one of the in-plane diffraction orders of the metasurface. Our study also shows that, contrary to the case of a non-diffractive metasurface, the lattice-plasmon-enabled asymmetric transmission can occur at normal incidence for cases where the metasurface is composed of chiral or achiral nanoparticles possessing 4-fold rotational symmetry.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.186
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1880.002

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 head0.069
GPT teacher head0.313
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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes1
Has abstractyes

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