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Record W2980722156 · doi:10.1038/s41467-019-12689-2

Ultrahigh resolution and color gamut with scattering-reducing transmissive pixels

2019· article· en· W2980722156 on OpenAlexafffund
June Sang Lee, Ji Yeon Park, Yong Hwan Kim, Seokwoo Jeon, Olivier Ouellette, Edward H. Sargent, Dong Ha Kim, Jerome K. Hyun

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningNational Research Foundation
KeywordsGamutMultispectral imageOpticsPlasmonStructural colorationPixelScatteringColor filter arrayMaterials scienceSurface plasmonOptoelectronicsPhysicsColor gelComputer sciencePhotonic crystalArtificial intelligenceNanotechnology

Abstract

fetched live from OpenAlex

Abstract While plasmonic designs have dominated recent trends in structural color, schemes using localized surface plasmon resonances and surface plasmon polaritons that simultaneously achieve high color vibrancy at ultrahigh resolution have been elusive because of tradeoffs between size and performance. Herein we demonstrate vibrant and size-invariant transmissive type multicolor pixels composed of hybrid TiO x -Ag core-shell nanowires based on reduced scattering at their electric dipolar Mie resonances. This principle permits the hybrid nanoresonator to achieve the widest color gamut (~74% sRGB area coverage), linear color mixing, and the highest reported single color dots-per-inch (58,000~141,000) in transmission mode. Exploiting such features, we further show that an assembly of distinct nanoresonators can constitute a multicolor pixel for use in multispectral imaging, with a size that is ~10-folds below the Nyquist limit using a typical high NA objective lens.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations43
Published2019
Admission routes2
Has abstractyes

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