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Record W2345043933 · doi:10.1021/acs.nanolett.6b01097

Super-Dispersive Off-Axis Meta-Lenses for Compact High Resolution Spectroscopy

2016· article· en· W2345043933 on OpenAlexaff
Mohammadreza Khorasaninejad, Wei Ting Chen, Jaewon Oh, Federico Capasso

Bibliographic record

VenueNano Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Waterloo
FundersAir Force Office of Scientific ResearchMinistry of Science and Technology, TaiwanCharles Stark Draper Laboratory
KeywordsOpticsMiniaturizationWavelengthPlanarMaterials scienceFocus (optics)Resolution (logic)WavefrontSpectral resolutionOptoelectronicsPhysicsComputer scienceSpectral lineNanotechnology

Abstract

fetched live from OpenAlex

Metasurfaces have opened a new frontier in the miniaturization of optical technology by allowing exceptional control over the wavefront. Here, we demonstrate off-axis meta-lenses that simultaneously focus and disperse light of different wavelengths with unprecedented spectral resolution. They are designed based on the geometric phase via rotated silicon nanofins and can focus light at angles as large as 80°. Due to the large angle focusing, these meta-lenses have superdispersive characteristics (0.27 nm/mrad) that make them capable of resolving wavelength differences as small as 200 pm in the telecom region. In addition, by stitching several meta-lenses together, we maintain a high spectral resolution for a wider wavelength range. The meta-lenses have measured efficiencies as high as 90% in the wavelength range of 1.1 to 1.6 μm. The planar and compact configuration together with high spectral resolution of these meta-lenses has significant potential for emerging portable/wearable optics technology.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.276
Teacher spread0.236 · 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

Citations227
Published2016
Admission routes1
Has abstractyes

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