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Record W4402996272 · doi:10.1021/acsnano.4c06983

Selective and Tunable Absorption of Twisted Light in Achiral and Chiral Plasmonic Metasurfaces

2024· article· en· W4402996272 on OpenAlexafffund
Ashish Jain, Howard Northfield, Ebrahim Karimi, Pierre Berini, V. R. Bhardwaj

Bibliographic record

VenueACS Nano · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsPlasmonMaterials scienceAbsorption (acoustics)OptoelectronicsMetamaterialNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

The symmetry of achiral metasurfaces suggests selective absorption is nonexistent when irradiated either by circularly polarized Gaussian or twisted light beams carrying orbital angular momentum (OAM). In chiral metasurfaces, the lack of symmetry leads to differential absorption when probed with chiral light either in the form of circular polarization (circular dichroism) or helical phase fronts (helical dichroism). Here, we demonstrate differential absorption of asymmetric twisted light beams, known as helical dichroism, which exist in an array and a single achiral structure and can be controlled. When extended to chiral structures, these asymmetrical chiral light modes enable to enhance and tune chiroptical sensitivity. Our technique offers more control parameters than just changing the OAM value, as presented in previous studies. Selective response to asymmetric helical light beams is qualitatively explained in terms of induced multipole moments. The presence of dichroism in achiral nanostructures offers a significant fabrication advantage over complex chiral structures and enables the development of next-generation plasmonic-based chiroptical spectroscopy and molecular sensing.

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 score0.006
Threshold uncertainty score0.359

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 studies0.0000.000
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 head0.012
GPT teacher head0.248
Teacher spread0.237 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

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