MétaCan
Menu
Back to cohort
Record W2360425439 · doi:10.1126/sciadv.1501258

Broadband and chiral binary dielectric meta-holograms

2016· article· en· W2360425439 on OpenAlexaff
Mohammadreza Khorasaninejad, Antonio Ambrosio, Pritpal S. Kanhaiya, Federico Capasso

Bibliographic record

VenueScience Advances · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Waterloo
FundersAir Force Office of Scientific ResearchGoogleNational Science Foundation
KeywordsHolographyBroadbandOpticsAchromatic lensBinary numberMaterials sciencePhase (matter)OptoelectronicsDispersion (optics)DielectricComputer sciencePhysics

Abstract

fetched live from OpenAlex

Subwavelength structured surfaces, known as meta-surfaces, hold promise for future compact and optically thin devices with versatile functionalities. By revisiting the concept of detour phase, we demonstrate high-efficiency holograms with broadband and chiral imaging functionalities. In our devices, the apertures of binary holograms are replaced by subwavelength structured microgratings. We achieve broadband operation from the visible to the near infrared and efficiency as high as 75% in the 1.0 to 1.4 μm range by compensating for the inherent dispersion of the detour phase with that of the subwavelength structure. In addition, we demonstrate chiral holograms that project different images depending on the handedness of the reference beam by incorporating a geometric phase. Our devices' compactness, lightness, and ability to produce images even at large angles have significant potential for important emerging applications such as wearable optics.

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 score0.003

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 communication0.0010.001
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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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

Citations347
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicMetamaterials and Metasurfaces ApplicationsFrench-language works237,207