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Record W4416193729 · doi:10.1002/adfm.202524783

MetaScope: Metasurface Based Multimodal Imaging System

2025· article· en· W4416193729 on OpenAlexaff
Isma Javed, Azhar Javed Satti, Juliano Katrib, Hanjun Cho, Hyemi Park, Khaled Mohamad Almustafa, Maad Ebrahim, Fadi Alzhouri, Muhammad Qasim Mehmood, Inki Kim

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversité de MontréalHôtel-Dieu de Montréal
FundersSamsungMinistry of Science and ICT, South KoreaNational Research Foundation
KeywordsBroadbandPolarization (electrochemistry)PhotonicsFocus (optics)SuperresolutionCircular polarizationHigh resolutionFeature (linguistics)

Abstract

fetched live from OpenAlex

Abstract Conventional approaches struggle to image tiny biological subjects because their features are too small, and the contrasts are too weak. Metalenses are simple alternatives that can capture all the data from a complex field with one device. A broadband optical metalens is presented, capable of polarization‐tunable, bright‐field (BF), edge‐enhanced (EE), and wide field‐of‐view (WFOV) imaging systems. The metalens has a feature resolution of 3.91 µm and focuses differently depending on the polarization state. When illuminated with linearly polarized light, it forms focus spots around the metalens suitable for high‐resolution WFOV imaging. When light with left and right circular polarization states (LCP or RCP) is incident, the focus shifts farther away, resulting in lower‐resolution contrast‐enhanced captures. The compact setup, which includes just a metalens and two polarizers, can easily be added to existing microscopes. This work holds significant potential for tiny, high‐performance bioimaging and integrated photonic computing devices.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.001

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 head0.010
GPT teacher head0.253
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2025
Admission routes1
Has abstractyes

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