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Record W4286499266 · doi:10.1515/nanoph-2022-0155

Toward a universal metasurface for optical imaging, communication, and computation

2022· article· en· W4286499266 on OpenAlexfundno aff
Prachi Thureja, Ruzan Sokhoyan, Claudio U. Hail, Jared Sisler, Morgan Foley, Meir Y. Grajower, Harry A. Atwater

Bibliographic record

VenueNanophotonics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsNanomaterialsMetamaterialComputationComputer scienceNanotechnologyOpticsMaterials scienceOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

In recent years, active metasurfaces have emerged as a reconfigurable nanophotonic platform for the manipulation of light. Here, application of an external stimulus to resonant subwavelength scatterers enables dynamic control over the wavefront of reflected or transmitted light. In principle, active metasurfaces are capable of controlling key characteristic properties of an electromagnetic wave, such as its amplitude, phase, polarization, spectrum, and momentum. A 'universal' active metasurface should be able to provide independent and continuous control over all characteristic properties of light for deterministic wavefront shaping. In this article, we discuss strategies for the realization of this goal. Specifically, we describe approaches for high performance active metasurfaces, examine pathways for achieving two-dimensional control architectures, and discuss operating configurations for optical imaging, communication, and computation applications based on a universal active metasurface.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.281
Teacher spread0.250 · 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

Citations59
Published2022
Admission routes1
Has abstractyes

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