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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.034
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.001

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