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Record W3126711713 · doi:10.1021/acsphotonics.0c01929

Photon Acceleration Using a Time-Varying Epsilon-near-Zero Metasurface

2021· article· en· W3126711713 on OpenAlexaff
Cong Liu, M. Zahirul Alam, Kai Pang, Karapet Manukyan, Orad Reshef, Yiyu Zhou, Saumya Choudhary, Joel G. Patrow, Anuj Pennathur, Hao Song, Zhe Zhao, Runzhou Zhang, Fatemeh Alishahi, Ahmad Fallahpour, Yinwen Cao, Ahmed Almaiman, Jahan M. Dawlaty, Moshe Tur, Robert W. Boyd, Alan E. Willner

Bibliographic record

VenueACS Photonics · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Ottawa
FundersDefense Advanced Research Projects Agency
KeywordsBlueshiftPhysicsOpticsExcitationPlasmonOptoelectronicsRefractive indexIndium tin oxideTerahertz radiationPhotonMaterials scienceThin filmPhotoluminescence

Abstract

fetched live from OpenAlex

A light beam’s frequency can blueshift when the beam travels through a medium that exhibits a time-dependent decrease in the refractive index. Here we show that a metasurface made of a plasmonic antenna array on a thin indium tin oxide (ITO), which exhibits epsilon-near-zero (ENZ) response, can behave as a time-varying medium and change the frequency of a sufficiently strong light beam through self-action effect. Specifically, we observe that a near-resonant optical excitation of the 92 nm thick metasurface leads to an intensity-dependent blueshift of the excitation pulse. We measured a maximum blueshift of ∼1.6 THz with ∼4 GW/cm 2 incident intensity. The observed effect using an ITO-based ENZ metasurface has an energy requirement that is up to 200× lower than implementations using ITO alone.

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 head0.047
GPT teacher head0.294
Teacher spread0.247 · 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

Citations55
Published2021
Admission routes1
Has abstractyes

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