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Record W4224250476 · doi:10.1002/ijch.202200007

Optical Resonances of Chiral Metastructures in the Mid‐infrared Spectral Range

2022· article· en· W4224250476 on OpenAlexaff
Denis A. B. Therien, Stuart Read, Scott M. Rosendahl, François Lagugné‐Labarthet

Bibliographic record

VenueIsrael Journal of Chemistry · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsCanadian Light Source (Canada)Western University
Fundersnot available
KeywordsChemistryPlasmonExcitationMetamaterialChirality (physics)Circular dichroismInfraredVibrational circular dichroismElectron-beam lithographyOpticsMolecular physicsPhysicsSymmetry breakingCrystallography

Abstract

fetched live from OpenAlex

Abstract Metamaterials with localized surface plasmon resonances have been the subject of intense research for a wealth of applications ranging from active photonic devices to analytical sensing units that utilizes the local field enhancement to improve sensitivity and decrease acquisition time. Herein, we focus on the modelled properties of a series of spiral metastructures that present multiple chiral plasmon modes in the 1–10 μm mid‐IR spectral range and that could be utilized to enhance vibrational circular dichroism measurements to further identify molecular, supramolecular and structural chirality centres. Finite‐difference time‐domain electromagnetic modelling was conducted to calculate the infrared spectra of the metastructures in response to a circularly polarized excitation. Several geometric parameters were altered to further tune the position of the resonances. In addition, the near‐field distribution of the optical resonances was calculated providing a spatial snapshot of the chiral modes. Preliminary spiral structures were fabricated using electron beam lithography and their vibrational circular response was measured.

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 score0.002

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 head0.016
GPT teacher head0.257
Teacher spread0.242 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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