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Record W4413394969 · doi:10.1021/acs.nanolett.5c03172

Assembling and Modeling Stacked Disordered Metasurfaces

2025· article· en· W4413394969 on OpenAlexaff
Miao Chen, Amit Sharma, Johann Michler, Xavier Maeder, Philippe Lalanne, Angelos Xomalis

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsInnovation Cluster (Canada)
FundersEuropean Research CouncilNorges ForskningsrådChina Scholarship CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Disordered metasurfaces offer unique properties unattainable with periodic or ordered metasurfaces, notably the absence of deterministic interference effects at specific wavelengths and angles. In this work, we introduce a lithography-free nanofabrication approach to realize cascaded disordered plasmonic metasurfaces with submicrometer total thickness. We experimentally characterize their angle-resolved specular and diffuse reflections using the bidirectional reflection distribution function and develop accurate theoretical models that remain valid even at large incidence angles. These models reveal the intricate interplay between coherent (specular) and incoherent (diffuse) scattering and demonstrate how coherent illumination can strongly influence the perceived color of diffusely scattered light. Exploiting this effect, we realize a centimeter-scale chromo-encryption device whose color changes depending on whether it is viewed under direct or diffuse illumination. Our results lay the groundwork for advanced nanophotonic platforms based on stacked disordered metasurfaces, offering versatile optical functionalities inaccessible with traditional multilayer thin-film technologies or single-layer metasurfaces.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.489

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)0.0000.000

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 head0.026
GPT teacher head0.283
Teacher spread0.258 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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