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Record W4220874504 · doi:10.1063/5.0080063

Disordered ensembles of strongly coupled single-molecule plasmonic picocavities as nonlinear optical metamaterials

2022· article· en· W4220874504 on OpenAlexaff
Felipe Herrera, Marina Litinskaya

Bibliographic record

VenueThe Journal of Chemical Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStrong Light-Matter Interactions
Canadian institutionsUniversity of British Columbia
FundersFondo Nacional de Desarrollo Científico y TecnológicoAgencia Nacional de Investigación y Desarrollo
KeywordsPlasmonMetamaterialNonlinear opticalMoleculeNonlinear systemNonlinear opticsPhysicsMaterials scienceMolecular physicsOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

We propose to use molecular picocavity ensembles as macroscopic coherent nonlinear optical devices enabled by nanoscale strong coupling. For a generic picocavity model that includes molecular and photonic disorder, we derive theoretical performance bounds for coherent cross-phase modulation signals using weak classical fields of different frequencies. We show that strong coupling of the picocavity vacua with a specific vibronic sideband in the molecular emission spectrum results in a significant variation of the effective refractive index of the metamaterial relative to a molecule-free scenario due to a vacuum-induced Autler–Townes effect. For a realistic molecular disorder model, we demonstrate that cross-phase modulation of optical fields as weak as 10 kW/cm2 is feasible using dilute ensembles of molecular picocavities at room temperature, provided that the confined vacuum is not resonantly driven by the external probe field. Our work paves the way for the development of plasmonic metamaterials that exploit strong coupling for optical state preparation and quantum control.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.001

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.253
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations10
Published2022
Admission routes1
Has abstractyes

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